Analyses from the early years

(A) List of Physics Analysis Projects (obsolete)

Who	Institution	Data	Topic
Jan Balewski	MIT	2009	W production
Michael Betancourt	MIT	2009/6	prompt gamma mid-rap A_LL/cross sec.
Alice Bridgeman	ANL	2009	EEMC gammas
Thomas Burton	Birmingham	2006	Lambda trans. pol.
Ramon Cendejas	UCLA/LBL	2008	di-jet cross section
Ross Corliss	MIT	2006/9	photons 1
Pibero Djawotho	TAMU	2009	inclusive and di-jet
Xin Dong	LBL
Jim Drachenberg	TAMU	2008	FMS+FTPC jets Sivers/Collins
Len Eun	PSU	2006/8	eta SSA
Robert Fersch	Kentucky	2009/6	tbd /mid-rapidity jet Collins
Oleksandr Grebenyuk	LBL	2009/6	TBD/pi0
Weihong He	IUCF	2006	EEMC pi0
Alan Hoffman	MIT	2005/6	neutral pions A_LL
Liaoyuan Huo	TAMU	2009	inclusive and di-jet
Christopher Jones	MIT	2009/6	inclusive jets A_LL/cross section
Adam Kocoloski	MIT	2005/6	charged pions A_LL
Priscilla Kurnadi	UCLA	2006	non photonic electron A_LL
William Leight	MIT	2009	Mid-rapidity hadron production
Xuan Li	Shandong U		hyperons
Brian Page	IUCF	2009	dijets
Donika Plyku	ODU	2009	Spin dep. in pp elastic scattering from pp2pp
Nikola Poljak	Zagreb	2006/8	Collins Sivers separation forward SSA
Tai Sakuma	MIT	2005/6	dijets cross section/A_LL
Joe Seele	MIT	2009	Ws and dijet cross section
Ilya Selyuzhenkov	IUCF	2006-9	Forward gamma-jet
David Staszak	UCLA	2006	Inclusive Jet A_LL
Justin Stevens	IUCF	2010	TBD
Naresh Subba	KSU	2006	Non-Photonic Elect. 1>eta>1.5 xsec
Matthew Walker	MIT	2006/9	dijets cross section/A_LL
Grant Webb	Kentucky	2009/6	mid-rap gamma or di-jet/ UEvent
Wei Zhou	Shandong U		hyperons
Wei-Ming Zhang	KSU	2008	non-photonic electrons EEMC A_LL

Common Analysis Trees

The Spin PWG maintains a set of trees connecting datasets from the various inclusive measurements in a way that allows for easy particle correlation studies. This page describes how to access the data in those trees.

Location

RCF:    /star/institutions/mit/common/run6/spinTree/
PDSF:   /auto/pdsfdv34/starspin/common/run6/spinTree/
Anywhere:   root://deltag5.lns.mit.edu//Volumes/scratch/common/run6/spinTree/spinAnalyses_runnumber.tree.root

The last option uses xrootd to access read-only files stored on an MIT server from any computer with ROOT installed. If you have an Intel Mac note that ROOT versions 5.13.06 - 5.14.00 have a bug (patched in 5.14.00/b) that prevents you from opening xrootd files.

Interactive Mode

The basic trees are readable in a simple interactive ROOT session. Each particle type is stored in a separate tree, so you need to use TTree::AddFriend to connect things together before you draw. For example:

 root [1] TFile::Open(&quot;root://deltag5.lns.mit.edu//Volumes/scratch/common/run6/spinTree/spinAnalyses_7156028.tree.root&quot;);  root [2] .ls                                                                             TXNetFile**             root://deltag5.lns.mit.edu//Volumes/scratch/common/run6/spinTree/spinAnalyses_7156028.tree.root   TXNetFile*             root://deltag5.lns.mit.edu//Volumes/scratch/common/run6/spinTree/spinAnalyses_7156028.tree.root    KEY: TProcessID       ProcessID0;1    00013b6e-72c3-1640-a0e8-e5243780beef    KEY: TTree    spinTree;1      Spin PWG common analysis tree    KEY: TTree    ConeJets;1      this can be a friend    KEY: TTree    ConeJetsEMC;1   this can be a friend    KEY: TTree    chargedPions;1  this can be a friend    KEY: TTree    bemcPions;1     this can be a friend  root [3] spinTree-&gt;AddFriend(&quot;ConeJets&quot;);                                                root [4] spinTree-&gt;AddFriend(&quot;chargedPions&quot;);  root [5] spinTree-&gt;Draw(&quot;chargedPions.fE / ConeJets.fE&quot;,&quot;chargedPions.fE&gt;0&quot;)

If you have the class definitions loaded you can also access member functions directly in the interpreter:

root [6] spinTree->Draw("chargedPions.Pt() / ConeJets.Pt()","chargedPions.Pt()>0")

Batch Mode

The StSpinTreeReader class takes care of all the details of setting branch addresses for the various particles behind the scenes. It also allows you to supply a runlist and a set of triggers you're interested in, and it will only read in the events that you care about. The code lives in

StRoot/StSpinPool/StSpinTree

and in the macros directory is an example showing how to configure it. Let's look at the macro step-by-step:

 //create a new reader  StSpinTreeReader *reader = new StSpinTreeReader();        //add some files to analyze, one at a time or in a text file  reader-&gt;selectDataset(&quot;$STAR/StRoot/StSpinPool/StSpinTree/datasets/run6_rcf.dataset&quot;);  //reader-&gt;selectFile(&quot;./spinAnalyses_6119039.tree.root&quot;);

Ok, so we created a new reader and told it we'd be using the files from Run 6 stored on RCF. You can also give it specfic filenames if you'd prefer, but there's really no reason to do so.

 //configure the branches you're interested in (default = true)  reader-&gt;connectJets             = true;  reader-&gt;connectNeutralJets      = false;  reader-&gt;connectChargedPions     = true;  reader-&gt;connectBemcPions        = true;  reader-&gt;connectEemcPions        = false;  reader-&gt;connectBemcElectrons    = false;  //optionally filter events by run and trigger  //reader-&gt;selectRunList(&quot;$STAR/StRoot/StSpinPool/StSpinTree/filters/run6_jets.runlist&quot;);  reader-&gt;selectRun(7143025);    //select events that passed hardware OR software trigger for any trigger in list  reader-&gt;selectTrigger(137221); reader-&gt;selectTrigger(137222);  reader-&gt;selectTrigger(137611);  reader-&gt;selectTrigger(137622);  reader-&gt;selectTrigger(5);    //we can change the OR to AND by doing  reader-&gt;requireDidFire      = true;  reader-&gt;requireShouldFire   = true;

In this block we configured the reader to pick up the jets, chargedPions and BEMC pi0s from the files. We also told it that we only wanted to analyze run 7132001, and that we only cared about events triggered by BJP1, L2jet, or L2gamma in the second longitudinal running period. Finally, we required that one of those trigIds passed both the hardware and the software triggers.

After that, the reader behaves pretty much like a regular TChain. The first time you call GetEntries() will be very slow (few minutes for the full dataset) as that's when the reader chains together the files and applies the TEventList with your trigger selection. Each of the particles is stored in a TClonesArray, and the StJetSkimEvent is accessible via reader->event().

 StJetSkimEvent *ev = reader-&gt;event();  TClonesArray *jets = reader-&gt;jets();  TClonesArray *chargedPions = reader-&gt;chargedPions();  TClonesArray *bemcPions = reader-&gt;bemcPions();    long entries = reader-&gt;GetEntries();  for(int i=0; i

What's Included?

Common trees are produced for both Run 5 and the 2nd longitudinal period of Run 6. Here's what available:

Run 5

skimEvent
ConeJets
chargedPions
bemcPions

Run 6

skimEvent
ConeJets12
ConeJetsEMC
chargedPions -- see (Data Collection)
bemcPions
bemcElectrons

Known Issues

The first time you read a charged pion (batch or interactive) you may see some messages like

Error in <tclass::new>: cannot create object of class StHelix</tclass::new>

These are harmless (somehow related to custom Streamers in the StarClassLibrary) but I haven't yet figured out how to shut them up.

42 runs need to be reprocessed for chargedPions in Run 5. Will do once Andrew gives the OK at PDSF.

40 runs need to be reprocessed for Run 6 because of MuDst problems. Murad has also mentioned some problems with missing statistics in the skimEvents and jet trees that we'll revisit at a later date.

Future Plans

Including EEMC pi0s and StGammaCandidates remains on my TO-DO list. I've also added into StJet a vector of trigger IDs fired by that jet. Of course we also need to get L2 trigger emulation into the skimEvent. As always, if you have questions or problems please feel free to contact me.

Cuts Summary

Here's a list of the cuts applied to the data in the common spin trees.

Run 5

Event

standard spinDB requirements
production triggers only

ConeJets

0.2 < detEta < 0.8
0.1 < E_neu / E_tot < 0.9

chargedPions

pt > 2
-1 < eta < 1
nFitPoints > 25
|DCA_global| < 1
-1 < nSigmaPion < 2

bemcPions

pt > 3.0
photon energies > 0.1
asymmetry < 0.8
0.08 < mass < 0.25
charged track veto
BBC timebin in {7,8,9}

Run 6

Event

standard spinDB requirements
production triggers + trigId 5 (L2gamma early runs)

ConeJets, ConeJetEMC -- no cuts applied

chargedPions

pt > 2
-1 < eta < 1
nFitPoints > 25
|DCA_global| < 1
-1 < nSigmaPion < 2

bemcPions

pt > 5.2
photon energies > 0.1
asymmetry < 0.8
0.08 < mass < 0.25
charged track veto
BBC timebin in {7,8,9} update: timebin 6 added in 2007-07-18 production
both SMD planes good

bemcElectrons added as of 2007-07-18 production

hardware or software trigger in (117001, 137213, 137221, 5, 137222, 137585, 137611, 137622)
Global dE/dx cut changing with momentum
nFitPoints >= 15
nDedxPoints >= 10
nHits / nPoss >= 0.52
track Chi2 < 4
DCAGlobal < 2
NEtaStrips > 1 && NPhiStrips > 1
Primary dE/dx cut changing with momentum
0.3 < P/E < 1.5
-0.01287 < PhiDist < 0.01345
ZDist in [-5.47,1.796] (West) or [-2.706,5.322] (East)

Introduction at Spin PWG meeting - 5/10/07

I've been working on a project to make the datasets from the various longitudinal spin analyses underway at STAR available in a common set of trees. These trees would improve our ability to do the kind of correlation studies that are becoming increasingly important as we move beyond inclusive analyses in the coming years.

In our current workflow, each identified particle analysis has one or more experts responsible for deciding just which reconstruction parameters and cuts are used to determine a good final dataset. I don't envision changing that. Rather, I am taking the trees produced by those analyzers as inputs, picking off the essential information, and feeding it into a single common tree for each run. I am also providing a reader class in StSpinPool that takes care of connecting the various branches and does event selection given a run list and/or trigger list.

Features

Readable without the STAR framework
Condenses data from several analyses down to the most essential ~10 GB (Run 6)
Takes advantage of new capabilities in ROOT allowing fast fill/run/trigger selection

Included Analyses

Event information using StJetSkimEvent
ConeJets12 jets (StJet only)
ConeJetsEMC jets (StJet only)
charged pions (StChargedPionTrack)
BEMC neutral pions (TPi0Candidate)
EEMC neutral pions (StEEmcPair?) -- TODO
electrons * -- TODO
...

Current Status

I'm waiting on the skimEvent reproduction to finish before releasing. I've got the codes to combine jets, charged pions, and BEMC pions, and I'm working with Jason and Priscilla on EEMC pions and BEMC electrons.

EEMC Direct Photon Studies (Pibero Djawotho, 2006-2008)

Everything as a single pdf file (341 pages, 8.2Mb)

2006.07.31 First Look at SMD gamma/pi0 Discrimination

Pibero Djawotho

Indiana University
July 31, 2006

Simulation

Simulation were done by Jason for the SVT review.

Maximal side residual


Figure 1: Fitted peak integral vs. fit residual sum (U+V) from st_jpsi input stream (J/psi trigger only).	Figure 2: Fitted peak integral vs. fit residual sum (U+V) from st_physics input stream (all triggers except express stream triggers).

xy distribution of SMD hits

The separation between photons and pions was achieved by using Les cut in the above figures where photons reside above the curve and pions below. The data set used is the st_jpsi express stream.

Single peak characteristics

Fit function

The transverse profile of an electromagnetic shower in the SMD can be parametrized by the equation below in each SMD plane:

f(x) is the energy in MeV as a function of SMD strip x. The algorithm performs a simultaneous fit in both the U and V plane. The maximal residual (data - fit) is then calculated. A single photon in the SMD should be well descibed by the equation above and therefore will have a smaller maximal residual. A neutral pion, which decays into two photons, should exhibit a larger maximal residual. Typically, the response would be a double peak, possibly a larger peak and a smaller peak corresponding to a softer photon.

Single event SMD response

This directory contains images of single event SMD responses in both U and V plane. The file name convention is SMD_RUN_EVENT.png. The fit function for a single peak is the one described in the section above with 5 parameters:

p0 = yield (P₀), area under the peak in MeV
p1 = mean (μ), center of peak in strips
p2 = sigma of the first Gaussian (w₁)
p3 = fraction of the amplitude of the second Gaussian with respect to the first one (B), fixed to 0.2
p4 = ratio of the width of the second Gaussian to the width of the first one (w₂/w₁), fixed to 3.5

Code

macros

Documents

2006.08.04 Second Look at SMD gamma/pi0 Discrimination

Second Look at SMD gamma/pi0 Discrimination

Pibero Djawotho
Indiana University
August 4, 2006

Dataset

The dataset used in this analysis is the 2005 p+p collision at √s=200 GeV with the endcap calorimeter high-tower-1 (eemc-ht1-mb = 96251) and high-tower-2 (eemc-ht2-mb = 96261) triggers.

The file catalog query used to locate the relevant files is:
get_file_list.pl -keys 'path,filename' -delim / -cond 'production=P05if, trgsetupname=ppProduction,filetype=daq_reco_MuDst,filename~st_physics, tpc=1,eemc=1,sanity=1' -delim 0

Results

SMD U and V Fits

Code

macros

2006.08.06 Comparison between EEMC fast and slow simulator

Comparison between EEMC fast and slow simulator

Pibero Djawotho
Indiana University
August 6, 2006

A detailed description of the EEMC slow simulator is presented at the STAR EEMC Web site.

The following settings were used in running the slow simulator:

  //--
  //-- Initialize slow simulator
  //--
  StEEmcSlowMaker *slowSim = new StEEmcSlowMaker("slowSim");
  slowSim->setDropBad(1);   // 0=no action, 1=drop chn marked bad in db
  slowSim->setAddPed(1);    // 0=no action, 1=ped offset from db
  slowSim->setSmearPed(1);  // 0=no action, 1=gaussian ped, width from db
  slowSim->setOverwrite(1); // 0=no action, 1=overwrite muDst values
  slowSim->setSource("StEvent");

  slowSim->setSinglePeResolution(0.1);
  slowSim->setNpePerMipSmd(2.0);
  slowSim->setNpePerMipPre(3.9);
  slowSim->setMipElossSmd(1.00/1000);
  slowSim->setMipElossPre(1.33/1000);

EEMC Fast Simulator	EEMC Slow Simulator

2006.09.15 Fit Parameters

Fit Parameters

Fit Function

The plots that follow are sums of individual SMD responses in each plane centered around a common mean (here 0), over a +/-40 strips range. The convention for the parameters in the fits below is:

p0=E -- area under the curve which represents energy in MeV
p1=μ -- mean
p2=σ_core -- width of the narrow Gaussian
p3=γ -- relative contribution of the wide Gaussian to the area/height
p4=σ_tail -- width of the wide Gaussian

Simulation

The simulation is from single photons thrown at the EEMC with the following p_T distribution:

The highest tower above 4 GeV in total energy is selected and the corresponding SMD sector fitted for peaks in both planes, where the area of the peaks in the U plane is constrained to be identical to that of the peak in the V plane. The peak is shifted to be centered at 0 where peaks from other events are then summed. The summed SMD response in each plane is displayed below:

Ditto in log scale.

Ditto by sector.

Fit Widths

Sector #	SMD-u σ_core	SMD-u σ_tail	SMD-v σ_core	SMD-v σ_tail
Sector 1	0.869033 ± 0.0142868	3.42031 ± 0.10226	0.84379 ± 0.0185107	3.03287 ± 0.0775009
Sector 2	0.814959 ± 0.0169271	2.99941 ± 0.0730426	0.889892 ± 0.0163065	3.35288 ± 0.0911979
Sector 3	0.84862 ± 0.0148706	3.07648 ± 0.0909689	0.914377 ± 0.014706	3.72821 ± 0.0966915
Sector 4	0.924398 ± 0.0144207	3.74458 ± 0.10611	0.888146 ± 0.0180771	3.06618 ± 0.0647075
Sector 5	0.934218 ± 0.0163887	3.45149 ± 0.0944309	0.911209 ± 0.0175273	3.28633 ± 0.0890581
Sector 6	0.797976 ± 0.0148133	3.20464 ± 0.0986085	0.822437 ± 0.018835	3.30595 ± 0.118813
Sector 7	0.836936 ± 0.0150085	3.28589 ± 0.0853598	0.873338 ± 0.0173883	3.16654 ± 0.0838938
Sector 8	0.828403 ± 0.0167005	3.05517 ± 0.075584	0.891045 ± 0.0152102	3.34806 ± 0.0836394
Sector 9	0.832881 ± 0.0127855	3.3214 ± 0.0762928	0.8436 ± 0.0175466	3.0183 ± 0.079444
Sector 10	0.804059 ± 0.0160906	3.0943 ± 0.0897946	0.874845 ± 0.015788	3.18113 ± 0.0748357
Sector 11	0.930286 ± 0.0187086	3.40024 ± 0.0951671	0.854395 ± 0.0167265	3.21076 ± 0.0812402
Sector 12	3.33227 ± 0.111911	0.848668 ± 0.0142344	0.895174 ± 0.0160939	3.48527 ± 0.12061

Data from 2005 pp200 EEMC HT 1 and 2 triggers

In this sample, high tower triggers, eemc-ht1-mb (96251) and eemc-ht2-mb (96261), from the 2005 p+p at √s=200 GeV ppProduction are selected. The highest tower above 4 GeV is chosen and the corresponding SMD sector is searched for peaks in both planes. Peaks from several events are summed together taking care of shifting them around to have a common mean.

Ditto in log scale.

Data from 2005 pp200 electrons

Here, I try to pick a representative sample of electrons from the 2005 pp200 dataset. The cuts used to pick out electrons are:

E_preshower1 > 5 MeV
E_preshower2 > 5 MeV
0.75 < p/E_tower < 1.25
3 < dE/dx < 4 keV/cm

The selection for electrons is illustrated in the dE/dx plot below, where the pions should be on the left and the electrons on the right.

Pibero Djawotho

Last modified Tue Aug 15 10:41:19 EDT 2006

2007.02.05 Reconstructed/Monte Carlo Photon Energy

Reconstructed/Monte Carlo Photon Energy

This study is motivated by Weihong's photon energy loss study where an eta-dependence of reconstructed photon energy to generated photon energy in EEMC simulation was observed.

In this study, the eta-dependence is investigated by running the EEMC slow simulator with the new readjusted weights for the preshower and postshower layers of the EEMC. Details on this are here.

Fit to a constant

Fit to a line

Fit to a quadratic

Comparison between Weihong's and Pibero's results

The parameters from the fits are used to plot the fit functions for comparison between Weihong's and Pibero's results.

Constant

Linear

Quadratic

Conclusion

While the adjusted weights for the different EEMC layers contribute to bringing the ratio of reconstructed energy to generated energy closer to unity, they do not remove the eta-dependence.

References

Pibero Djawotho

Last updated Mon Feb 5 10:10:42 EST 2007

2007.02.08 E_reco / E_mc vs. eta

E_reco / E_mc vs. eta

Legend

black curve: before EEMC slow simulator
red curve: after EEMC slow simulator

Jason's Monte Carlo

4.4k single gamma's
No SVT
Nominal vertex
Flat in pt 4-12 GeV

Will's Monte Carlo

10k single gamma's
SVT/SSD out
Vertex at 0
Flat in pt 5-60 GeV

In the plot below, I use the energy of the single tower (tower with max energy) presumably the tower the photon hit. The nonlinearity seems to disappear.

In the plot below, I use the energy of the 3x3 cluster of tower centered around the tower with the max energy. The nonlinearity is restored.

The plot below shows E_tower/E_cluster vs. eta where the cluster consists of 3x3 towers centered around the max energy tower.

Below is the profile of E_tower/E_cluster vs. eta.

The plot below shows the energy sampled by the entire calorimeter as a function of eta, i.e. sampling fraction as a function of eta.

Sampling fraction integrated over all eta's.

Pibero Djawotho

Last updated Thu Feb 8 13:59:29 EST 2007

2007.02.11 Reconstructed/Monte Carlo Muon Energy

Reconstructed/Monte Carlo Muon Energy

10k muons thrown by Will with:

z_vertex=0
Flat in p_T 5-60 GeV/c
Flat in η 1.1-2

z_vertex

p_T vs. η

E_MC

E_tower/E_MC vs. η

E_cluster/E_MC vs. η

E_towertanh(eta) vs. eta

Pibero Djawotho

Last modified Sun Feb 11 19:51:55 EST 2007

2007.02.15 160 GeV photons

160 GeV photons

10k 160 GeV photons
z_vertex=0
η range 0.8-2.2
SVT/SSD out

Pibero Djawotho

Last updated Thu Feb 15 04:33:09 EST 2007

2007.02.15 20 GeV photons

20 GeV photons

10k 20 GeV photons
z_vertex=0
η range 0.8-2.2
SVT/SSD out

Pibero Djawotho

Last updated Thu Feb 15 04:32:25 EST 2007

2007.02.15 80 GeV photons

80 GeV photons

10k 80 GeV photons
z_vertex=0
η range 0.8-2.2
SVT/SSD out

Pibero Djawotho

Last updated Thu Feb 15 03:16:54 EST 2007

2007.02.15 Reconstructed/Monte Carlo Electron Energy

Reconstructed/Monte Carlo Electron Energy

E=1 GeV	E=2 GeV

Pibero Djawotho

Last modified Thu Feb 15 00:42:30 EST 2007

2007.02.19 10 GeV photons

10 GeV photons

10k 10 GeV photons
z_vertex=0
η range 0.8-2.2
SVT/SSD out

Pibero Djawotho

Last updated Mon Feb 19 20:35:13 EST 2007

2007.02.19 40 GeV photons

40 GeV photons

10k 40 GeV photons
z_vertex=0
η range 0.8-2.2
SVT/SSD out

Pibero Djawotho

Last updated Mon Feb 19 20:37:37 EST 2007

2007.02.19 5 GeV photons

5 GeV photons

10k 5 GeV photons
z_vertex=0
η range 0.8-2.2
SVT/SSD out

Pibero Djawotho

Last updated Mon Feb 19 20:13:39 EST 2007

2007.02.19 Summary of Reconstructed/Monte Carlo Photon Energy

Summary of Reconstructed/Monte Carlo Photon Energy

Description

The study presented here uses Monte Carlo data sets generated by Will Jacobs at different photon energies (5, 10, 20, 40, 80, 160 GeV):

10k photons
vertex at 0
eta 0.8-2.2
SVT/SSD out

For each photon energy, the ratio E_reco/E_MC vs. eta was plotted and fitted to the function p0+p1*(1-eta), where E_reco is the reconstructed photon energy integrated over the

entire

EEMC. The range of the fit was fixed from 1.15 to 1.95 to avoid EEMC edge effects. The advantage of parametrizing the eta-dependence of the ratio in this way is that p0 is immediately interpretable as the ratio in the middle of the EEMC. The parameters p0 and p1 vs. photon energy were subsequently plotted for the EEMC fast and slow simulator.

EEMC Fast/Slow Simulator Results

Conclusions

The parameter p0, i.e. the ratio E_reco/E_MC in the mid-region of the EEMC, increases monotonically from 0.74 at 5 GeV to 0.82 at 160 GeV, and the parameter p1, i.e. the slope of the eta-dependence, also increases monotonically from -0.035 at 5 GeV to 0.038 at 160 GeV. There appears to be a magic energy around 10 GeV where the response of the EEMC is nearly flat across its entire pseudorapidity range. The anomalous slope p1 at 160 GeV for the EEMC slow simulator is an EEMC hardware saturation effect. The EEMC uses 12-bit ADC's for reading out tower transverse energies and is set for a 60 GeV range. Any particle which deposits more than 60 GeV in E_T will be registered as depositing only 60 GeV as the ADC will return the maximum value of 4095. This translates into a limit on the eta range of the EEMC for a particular energy. Let's say that energy is 160 GeV and the EEMC tops at 60 GeV in E_T, then the minimum eta is acosh(160/60)=1.6. This limitation is noticeable in a plot of E_reco/E_MC vs. eta. This anomaly is not observed in the result of the EEMC fast simulator because the saturation behavior was not implemented at the time of the simulation (it has since been corrected). The energy-dependence of the parameter p0 is fitted to p0(E)=a+b*log(E) and the parameter p1 to p1(E)=a+b/log(E). The results are summarized below:

EEMC fast simulator fit p0(E)=a+b*log(E)
a = 0.709946 +/- 0.00157992
b = 0.022222 +/- 0.000501239

EEMC slow simulator fit p0(E)=a+b*log(E)
a = 0.733895 +/- 0.00359237
b = 0.0177537 +/- 0.0011397

EEMC fast simulator fit p1(E)=a+b/log(E)
a = 0.0849103 +/- 0.00556979
b = -0.175776 +/- 0.0138241

EEMC slow simulator fit p1(E)=a+b/log(E)
a = 0.0841488 +/- 0.0052557
b = -0.187769 +/- 0.0130445

Pibero Djawotho

Last updated Mon Feb 19 23:18:44 EST 2007

2007.05.24 gamma/pi0 separation in EEMC using linear cut

gamma/pi0 separation in EEMC using linear cut

Pibero Djawotho

Last updated Thu May 24 04:41:13 EDT 2007

2007.05.24 gamma/pi0 separation in EEMC using quadratic cut

gamma/pi0 separation in EEMC using quadratic cut

Pibero Djawotho

Last updated Thu May 24 04:41:13 EDT 2007

2007.05.24 gamma/pi0 separation in EEMC using quadratic cut

gamma/pi0 separation in EEMC using quadratic cut

Pibero Djawotho

Last updated Thu May 24 04:41:13 EDT 2007

2007.05.30 Efficiency of reconstructing photons in EEMC

Efficiency of reconstructing photons in EEMC

Monte Carlo sample

10k photons
STAR y2006 geometry
z-vertex=0
Flat in pt 10-30 GeV
Flat in eta 1.0-2.1

SMD gamma/pi0 discrimination algorithm

The following

slide

from the IUCF STAR Web site gives a brief overview of the SMD gamma/pi0 discrimination algorithm using the method of maximal sided fit residual (data - fit). This technique comes to STAR EEMC from the Tevatron via Les Bland via Jason Webb. The specific fit function used in this analysis is:

f(x)=[0]*(0.69*exp(-0.5*((x-[1])/0.87)**2)/(sqrt(2*pi)*0.87)+0.31*exp(-0.5*((x-[1])/3.3)**2)/(sqrt(2*pi)*3.3))

x is the strip id in the SMD-u or SMD-v plane. The widths of the narrow and wide Gaussians are determined from empirical fits of shower shape response in the EEMC from simulation.

Optimizing cuts for gamma/pi0 separation

In the rest of this analysis, only those photons which have reconstructed pt > 5 GeV are kept. There is no requirement that the photon doesn't convert. The dividing curve between photons and pions is:

f(x)=4*x+1e-7*x**5

The y-axis is integrated yield over the SMD-u and SMD-v plane, and the x-axis is the sum of the maximal sided residual of the SMD-u and SMD-v plane.

Following exchanges with Scott Wissink, the idea is to move from a quintic to a quadratic to reduce the number of parameters. In addition, the perpendicular distance between the curve and a point in the plane is used to estimate the likelihood of a particle being a photon or pion. Distances above the curve are positive and those below are negative. The more positive the distance, the more likely the particle is a photon. The more negative the distance, the more likely the particle is a pion.

Hi Pibero,

With your new "linear plus quintic" curve (!) ... how did you choose the
coefficients for each term?  Or even the form of the curve?  I'm not
being picky, but how to optimize such curves will be an important issue
as we (hopefully soon) move on to quantitative comparisons of efficiency
vs purity.

As a teaser, please see attached - small loss of efficiency, larger gain
in purity.

Scott

Hi Pibero,

I just worked out the distance of closest approach to a curve of the form

    y(x) = a + bx^2

and it involves solving a cubic equation - so maybe not so trivial after
all.  But if you want to pursue this (not sure it is your highest
priority right now!), the cubic could be solved numerically and "alpha"
could be easily calculated.

More fun and games.

Scott

Hi Pibero,

I played around with the equations a bit more, and I worked out an
analytic solution.  But a numerical solution may still be better, since
it allows more flexibility in the algebraic form of the 'boundary' line
between photons and pions.

Here's the basic idea:  suppose the curved line that cuts between
photons and pions can be expressed as y = f(x).  If we are now given a
point (x0,y0) in the plane, our goal is to find the shortest distance to
this line.  We can call this distance d (I think on your blackboard we
called it alpha).

To find the shortest distance, we need a straight line that passes
through (x0,y0) and is also perpendicular to the curve f(x).  Let's
define the point where this straight line intersects the curve as
(x1,y1).  This means (comparing slopes)

    (y1 - y0) / (x1 - x0) = -1 / f'(x1)

where f'(x1) is the derivative of f(x) evaluated at the point (x1,y1). 
Rearranging this, and using y1 = f(x1), yields the general result

    f(x1) f'(x1)  -  y0 f'(x1)  +   x1  -   x0  =  0

So, given f(x) and the point (x0,y0), the above is an equation in only
x1.  Solve for x1, use y1 = f(x1), and then the distance d of interest
is given by

    d = sqrt[ (x1 - x0)^2 + (y1 - y0)^2 ]

Example:  suppose we got a reasonable separation of photons and pions
using a curve of the form

    y = f(x) = a + bx^2

Using this in the above general equation yields the cubic equation

    (2b^2) x1^3  +  (2ab + 1 - 2by0) x1  -  x0  =  0

Dividing through by 2b^2, we have an equation of the form

    x^3 + px + q = 0

This can actually be solved analytically - but as I mentioned, a
numerical approach gives us more flexibility to try other forms for the
curve, so this may be the way to go.  I think (haven't proved
rigorously) that for positive values of the constants a, b, x0, and y0,
the cubic will yield three real solutions for x1, but only one will have
x1 > 0, which is the solution of interest.

Anyway, it has been an interesting intellectual exercise!

Scott

I made use of the ROOT function TMath::RootsCubic to solve the cubic equation numerically for computing distances of each point to the curve. With the new quadratic curve f(x)=100+0.1*x^2 the efficiency is 63% and the rejection is 82%.

Efficiency and Rejection

The plot on the left below shows the efficiency of identifying photons over the pt range of 10-30 GeV and the one on the right shows the rejection rate of single neutral pions. Both average about 75% over the pt range of interest.

Rejection vs. efficiency at different energies

The plot below shows background rejection vs. signal efficiency for different energy ranges of the thrown gamma/pi0.

Rejection vs. efficiency with preshower cut

Below on the left is a plot of the ratio of the sum of preshower 1 and 2 to tower energy for both photons (red) and pions (blue). On the right is the rejection of pions vs. efficiency of photons as I cut on the ratio of preshower to tower. It is clear from these plots that the preshower layer is not a good gamma/pi0 discriminator, although can be used to add marginal improvement to the separation preovided by the shower max.

ALL ENERGIES

E=20-40 GeV

E=40-60 GeV

E=60-80 GeV

E=80-90 GeV

Pibero Djawotho

Last updated Wed May 30 00:32:16 EDT 2007

2007.06.12 gamma/pi0 separation in EEMC at pT 5-10 GeV

gamma/pi0 separation in EEMC at pT 5-10 GeV

Pibero Djawotho

Last updated Tue Jun 12 11:59:42 EDT 2007

2007.06.28 Photons in Pythia

Pythia Simulations

All partonic pT

The plots below show the distribution of clusters in the endcap calorimeter for different partonic pT ranges. 2000 events were generated for each pT range. A cluster is made up of a central high tower above 3 GeV in pT and its surounding 8 neighbors. The total cluster pT must exceed 4.5 GeV.

pT=9-11 GeV

Below is the pT of direct and decay photons from the Pythia record. Note how the two subsets are well separated at a given partonic pT. Any contamination to the direct photon signal would have to come from higher partonic pT.

Differences between Renee's and Manuel's Pythia records?

Number of prompt photons per event from GEANT record

Pibero Djawotho

Last updated Fri Jun 8 16:08:27 EDT 2007

a_LL

Partonic a_LL

Jet	Gamma

Pibero Djawotho

Last updated Sat Jun 30 20:14:21 EDT 2007

gamma pT=9-11 GeV

Pibero Djawotho

Last modified Fri Jul 6 10:48:39 EDT 2007

gamma-jet kinematics

Clusters without parent track

Pibero Djawotho

Last updated Thu Jun 28 04:43:57 EDT 2007

gamma/X separation by energy

Pibero Djawotho

Last updated Wed Jul 11 11:10:26 EDT 2007

gamma/X separation by energy with pT weights

Pibero Djawotho

Last updated Thu Jul 12 00:28:35 EDT 2007

gamma/X separation by energy with pT weights and normalized by number of events

Pibero Djawotho

Last updated Wed Jul 18 14:55:08 EDT 2007

gamma/pi0 separation efficiency and rejection at pT=5-7 GeV

Pibero Djawotho

Last updated Wed Jul 4 17:45:26 EDT 2007

gamma/pi0 separation efficiency and rejection at pT=9-11 GeV

Pibero Djawotho

Last updated Wed Jul 4 13:23:41 EDT 2007

gamma/pi0 separation efficiency and rejection at pT=9-11 GeV

Pibero Djawotho

Last updated Wed Jul 4 13:23:41 EDT 2007

2007.07.09 How to run the gamma fitter

How to run the gamma fitter

The gamma fitter runs out of the box. The code consists of the classes StGammaFitter and StGammaFitterResult in CVS. After checking out a copy of offline/StGammaMaker, cd into the offline directory and run:

root4star StRoot/StGammaMaker/macros/RunGammaFitterDemo.C

The following plots will be generated on the ROOT canvas and dumped into PNG files.

Pibero Djawotho

Last modified Mon Jul 9 18:40:07 EDT 2007

2007.07.25 Revised gamma/pi0 algorithm in 2006 p+p collisions at sqrt(s)=200 GeV

Revised gamma/pi0 algorithm in 2006 p+p collisions at sqrt(s)=200 GeV

Description

The class

StGammaFitter

computes the maximal sided residual of the SMD response in the u- and v-plane for gamma candidates. It is based on C++ code developed by Jason Webb from the original code by Les Bland who got the idea from CDF (?) The algorithm follows the steps below:

The SMD response, which is SMD strips with hits in MeV, in each plane (U and V) is stored in histogram hU and hV.
Fit functions fU and fV are created. The functional form of the SMD peak is a double-Gaussian with common mean and fixed widths. The widths were obtained by the SMD response of single photons from the EEMC slow simulator. As such, the only free parameters are the common mean and the total yield. The actual formula used is: [0]*(0.69*exp(-0.5*((x-[1])/0.87)**2)/(sqrt(2*pi)*0.87)+0.31*exp(-0.5*((x-[1])/3.3)**2)/(sqrt(2*pi)*3.3))
- [0] = yield
- [1] = mean
The mean is fixed to the strip with maximum energy and the yield is adjusted so the height of the fit matches that of the mean.
The residual for each side of the peak is calculated by subtracting the fit from the data (residual = data - fit) from 2 strips beyond the mean out to 40 strips.
The maximal sided residual is the greater residual of each side.

Code

Candidates selection

2006 p+p at 200 GeV dataset from Sivers analysis (from Jan Balewski)
/star/institutions/iucf/balewski/prodOfficial06_muDst/
Gamma candidate from gamma maker: 3x3 clusters with pt > 5 GeV
No track pointing to cluster
Minimum of 3 SMD hits in each plane
Cuts from Jan & Naresh electron analysis:
- Preshower 1 energy > 0.5 MeV
- Preshower 2 energy > 2.0 MeV
- Postshower energy < 0.5 MeV
The triggers caption in the PDF files shows the trigger id's satisfied by the event. A red trigger id is a L2-gamma trigger. I observe that generally the L2-gamma triggered event are a bit cleaner. Also shown is the pt and energy of the cluster.

Raw SMD response

The parameters of the isolation cut were suggested by Steve Vigor:

Hi Pibero,

  In general, I believe people have used smaller cone radii for isolation
cuts than for jet reconstruction (where the emphasis is on trying to
recover full jet energy).  So you might try something like requiring
that no more than 10 or 20% of the candidate cluster E_T appears
in scalar sum p_T for tracks and towers within a cone radius of
0.3 surrounding the gamma candidate centroid, excluding the
considered cluster energy.  The cluster may already contain energy
from other jet fragments, but that should be within the purview of
the gamma/pi0 discrimination algo to sort out.  For comparison, Les
used a cone radius of 0.26 for isolation cuts in his original simulations
of gamma/pi0 discrimination with the endcap.  Using much larger
cone radii may lead to accidental removal of too many valid gammas.


Steve

Pibero Djawotho

Last updated Wed Jul 25 10:07:07 EDT 2007

2007.09.12 Endcap Electrons

Endcap Electrons

This analysis is based on the work of Jan and Justin on SMD Profile Analysis for different TPC momenta. See here for a list of cuts. The original code used by Jan and Justin is here.

Transverse running

Analysis uses 64 out of 300 runs from 2006 pp transverse run

MuDst are located at:

/star/institutions/iucf/balewski/prodOfficial06_muDst/

No trigger selection

Figure 1: Number of tracks surviving each successive cut

Figure 2a: Number of tracks per trigger id for all electron candidates. Most common trigger ids are:

127652	eemc-jp0-etot-mb-L2jet	EEMC JP > th0 (32, 4 GeV) and ETOT > TH (109, 14 GeV), minbias condition, L2 Jet algorithm, reading out slow detectors, transverse running
127271	eemc-jp1-mb	EEMC JP > th1 (49, 8 GeV) && mb, reading out slow detectors, transverse running
127641	eemc-http-mb-l2gamma	EEMC HT > th1 (12, 2.6 GeV, run < 7100052;13, 2.8 GeV, run >=7100052) and TP > TH1 (17, 3.8 GeV, run < 710052; 21, 4.7 GeV, run>=7100052 ), minbias condition, L2 Gamma algorithm, reading out slow detectors, L2 thresholds at 3.4, 5.4, transverse running
127622	bemc-jp0-etot-mb-L2jet	BEMC JP > th0 (42, 4 GeV) and ETOT > TH (109, 14 GeV), minbias condition, L2 Jet algorithm, reading out slow detectors, transverse running; L2jet thresholds at 8.0,3.6,3.3

Figure 2b: Number of tracks per trigger id for all electron candidates for p_T > 4 GeV. The dominant trigger ids become:

127641	eemc-http-mb-l2gamma	EEMC HT > th1 (12, 2.6 GeV, run < 7100052;13, 2.8 GeV, run >=7100052) and TP > TH1 (17, 3.8 GeV, run < 710052; 21, 4.7 GeV, run>=7100052 ), minbias condition, L2 Gamma algorithm, reading out slow detectors, L2 thresholds at 3.4, 5.4, transverse running
127262	eemc-ht2-mb-emul	EEMC HT > th2 (22, 5.0 GeV) && mb, reading out slow detectors, emulated in L2, transverse running, different threshold from 117262
127271	eemc-jp1-mb	EEMC JP > th1 (49, 8 GeV) && mb, reading out slow detectors, transverse running
127652	eemc-jp0-etot-mb-L2jet	EEMC JP > th0 (32, 4 GeV) and ETOT > TH (109, 14 GeV), minbias condition, L2 Jet algorithm, reading out slow detectors, transverse running

Figure 3: p_T distribution of tracks before E/p, dE/dx and pT cut

Figure 4: p_T distribution of electron candidates with p_T > 4 GeV

Figure 5: η distribution of electron candidates (all p_T)

Figure 6: φ distribution of electron candidates (all p_T)

Figure 7: dE/dx of tracks before E/p and dE/dx cuts (all p_T)

Figure 8: dE/dx of tracks before E/p and dE/dx cuts (p_T > 4 GeV)

Figure 9: dE/dx of tracks before E/p and dE/dx cuts (all p_T and 0.8 < η < 1.0)

Figure 10: dE/dx of tracks before E/p and dE/dx cuts (all p_T and 1.0 < η < 1.2)

Figure 11: dE/dx of tracks before E/p and dE/dx cuts (all p_T and 1.2 < η < 1.4)

Figure 12: dE/dx of tracks before E/p and dE/dx cuts (all p_T and 1.4 < η < 1.6)

Figure 13: dE/dx of tracks before E/p and dE/dx cuts (all p_T and 1.6 < η < 1.8)

Figure 14: dE/dx of tracks before E/p and dE/dx cuts (all p_T and 1.8 < η < 2.0)

Click here for SMD profiles of transverse electron candidates.
Click here for ROOT file with transverse electrons ntuple.

Longitudinal running

MuDst are located at:

/star/institutions/iucf/hew/2006ppLongRuns/

Figure 2.1

Figure 2.2: The dominant trigger ids are:

137273	eemc-jp1-mb	EEMC JP > th1 (52, 8.7 GeV) && mb, reading out slow detectors, longitudinal running 2
137641	eemc-http-mb-l2gamma	EEMC HT > th1 (16, 3.5 GeV) and TP > th1 (20, 4.5 GeV), minbias condition, L2 Gamma algorithm, reading out slow detectors, L2 thresholds at 3.7, 5.2, longitudinal running 2
137262	eemc-ht2-mb-emul	EEMC HT > th2 (22, 5.0 GeV) && mb, reading out slow detectors, emulated in L2, longitudinal running 2
137222	bemc-jp1-mb	BEMC JP > th1 (60, 8.3 GeV) && mb, reading out slow detectors, longitudinal running 2

Figure 2.3a

Figure 2.3

Figure 2.4

Figure 2.5

Figure 2.6

Figure 2.7

Figure 2.8

Figure 2.9

Figure 2.10

Click here for SMD profiles of longitudinal electron candidates.
Click here for ROOT file with longitudinal electrons ntuple.

Code

Click here for a tarball of the code used in this analysis.

Summed SMD response

SMD response function

f(x)=p0*(0.69*exp(-0.5*((x-p1)/0.87)**2)/(sqrt(2*pi)*0.87)+0.31*exp(-0.5*((x-p1)/3.3)**2)/(sqrt(2*pi)*3.3))

```
p0 = yield
```
```
p1 = centroid
```

Transverse

21 electrons

Longitudinal

99 electrons

Pibero Djawotho

Last updated Wed Sep 12 08:29:53 EDT 2007

2008.01.23 Endcap etas

Endcap etas

This analysis to look for etas at higher energy is in part motivated by this study. The interest in etas, of course, is that their decay photons are well separated at moderate energies (certainly more separated than the photons from pi0 decay). I ran Weihong's pi0 finder with tower seed threshold of 0.8 GeV and SMD seed threshold of 5 MeV (I believe his default SMD seed setting is 2 MeV). I then look in the 2-photon invariant mass region between 0.45 and 0.65 GeV (the PDG nominal mass for the eta is 0.54745 +/- 0.00019 GeV). I observe what looks like a faint eta peak. The dataset processed is the longitudinal 2 run of 2006 from the 20 runs sitting on the IUCF disk in Weihong's directory (/star/institutions/iucf/hew/2006ppLongRuns/).

Within the reconstructed mass window 0.45 to 0.65 GeV, I take a look at the decay photon shower profiles in the SMD. The samples are saved in the file etas.pdf. For the most part, these shower shapes are cleaner than the original sample. Although the statistics are not great.

Additional Material

Documents

Pibero Djawotho

Last updated Wed Jan 23 12:35:43 EST 2008

2008.02.27 ESMD shape library

ESMD shape library

Shower Widths for Monte Carlo and Data

Description

Hal did a comparison of the widths of the shower shapes between Monte Carlo and data. Below is a description of what was done.

      I took the nominal central value, either from the maxHit or the
nominal central value, and added the energy in the +/- 12 strips.  Then I
computed the mean strip (which may have been different from the nominal
central value!!).  I normalized the shape to give unit area for each smd
cluster, and added to the histograms separately for U and V and for MC and
data (= Will's events).  I did NOT handle Will's events correctly, just
using whatever event was chosen randomly, rather than going through his
list sequentially.  Note I ran 1000 events, and got 94 events in my shower
shape histos.

      So, there are several minor problems.  1) I didn't go through Will's
events sequentially.  2) I normalized, but perhaps not to the correct 25
strips, because the mean strip and the nominal strip may have differed.
3) there may have been a cutoff on some events due to being close to one
end of the smd plane (near strip 0 or 287).  My sense from looking at the
plots is that these don't matter much.

      The conclusion is that the MC shape is significantly narrower than
the shape from Will's events, which is obviously narrower than the random
clusters we were using at first with no selection for the etas.  Hence, we
are not wasting our time with this project.

Decsription of Pythia Sample

A few histograms were added to the code:

MC is Pythia gamma-jet at partonic pT 9-11 GeV with gamma in the Endcap
Data is from Will Jacobs golden events from Weihong sample
Require no conversion
Require all hits from direct photon in same sector

Figure 1:

Data vs. MC mean u-strip

Figure 2:

Data vs. MC mean v-strip

Figure 3:

Data vs. MC u-strip sigma

Figure 4:

Data vs. MC v-strip sigma

Figure 5:

MC E

vs. E

_u

Figure 6:

Data E

vs. E

_u

Figure 7:

MC energy asymmetry in SMD planes

Figure 8:

Data energy asymmetry in SMD planes

Figure 9:

Shower shape library index used (picked at random)

Single events shower shapes are displayed in

esmd.pdf

esmd_solid.pdf

green = projected position of direct photon in the Endcap
blue = Monte Carlo SMD response
red = Data SMD response

Hal Spinka

Pibero Djawotho

Last modified Wed Feb 27 09:51:27 EST 2008

2008.02.28 ESMD QA for run 7136033

ESMD QA for run 7136033

Pibero Djawotho

Last updated Thu Feb 28 16:17:55 EST 2008

2008.03.04 A second look at eta mesons in the STAR Endcap Calorimeter

A second look at eta mesons in the STAR Endcap Calorimeter

Introduction

In case you missed it, the first look is

here

. I processed

44 runs

from the 2006 pp longitudinal 2 runs and picked events tagged with the L2gamma trigger id (137641). I ran the StGammaMaker on the MuDst files from these runs and produced gamma trees. These gamma trees are available at

/star/institutions/iucf/pibero/2007/etaLong/

. Within the StGammaMaker framework, I developed code to seek candidate etas with emphasis on high purity. The macros and source files are:

Note, the workhorse function is

StEtaFinder::findTowerPoints()

Algorithm

Find seed tower with pT > 0.8 GeV
Require no TPC track into the seed tower
Get the ranges of SMD U & V strips that span the volume of the seed tower
Find the strips with maximum energy within these ranges
Require that the maximum strips have more than 2 MeV in each SMD plane
Get the intersection of the maximum strips and ensures that it lies within 70% of the fiducial volume of the seed tower
Make sure the photon candidate responsible for the SMD clusters above enters and exits the same seed tower
Form a 11-strip cluster in each plane with +/-5 strips around the max strip and require that it contains 70% of the energy in a range +/-20 strips around the max strip
Require that the energy asymmetry between the 11-strip clusters in the U and V planes be less than 20%
Create a point using the energy of the seed tower and the position of the intersection of the max strips in the SMD U and V planes
Repeat until seed towers in the event are exhausted
Combine different points in the event to calculate the invariant mass
Diphoton pairs with invariant mass between 0.4 and 0.6 GeV are saved to a PDF file

Invariant mass

I fit the diphoton invariant mass with two Gaussians, one for the pi0 peak (p0-p2) and another one for the eta peak (p3-p5) plus a quadratic for the background (p6-p8). The Gaussian is of the form p0*exp(0.5*((x-p1)/p2)**2) and the quadratic is of the form p6*+p7*x+p8*x**2. A slightly better chi2/ndf in the fit is achieved by using Breit-Wigner functions instead of Gaussians for the signal here. I calculate the raw yield of etas from the fit as p3*sqrt(2*pi)*p5/bin_width = 85 where each bin is 0.010 GeV wide. I select candidate etas in the mass range 0.45 to 0.55 GeV and plot their photon response in the shower maximum detector here. Since we are interested in collecting photons of pT > 7 GeV, only those candidate photons with pT > 5 GeV will be used in the shower shape library. I also calculate the background under the signal region by integrating the background fit from 0.45 to 0.55 GeV and get 82 counts.

S = 85
B = 82
S:B = 1.03:1
S/√S+B = 6.6

Additional plots

PhotonsFromEtas.txt
PhotonsFromEtas2.txt

Pibero Djawotho

Last updated at Tue Mar 4 16:21:13 EST 2008

2008.03.08 Adding the SMD energy to E_reco/E_MC for Photons

Adding the SMD energy to E_reco/E_MC for Photons

The following is a revisited study of E_reco/E_MC for photons with the addition of the SMD energy to E_reco.

QA plots for each energy

E_SMD/E_reco vs. eta

Pibero Djawotho

Last updated Thu Mar 8 04:27:28 EST 2007

2008.03.21 Chi square method

Chi square method

 SectorVsRunNumber.png   10-Feb-2010 12:22   14K  
 ShowerShapes.png        10-Feb-2010 12:22   17K  
 chiSquareMC.png         10-Feb-2010 12:22   13K  
 chiSquarePibero.png     10-Feb-2010 12:22   16K  
 chiSquareWill.png       10-Feb-2010 12:22   16K  
 chiSquareWillAndMC.png  10-Feb-2010 12:22   17K

2008.04.08 Data-Driven Shower Shapes

Data-Driven Shower Shapes

Gamma Conversion before the Endcap

The plots below show the conversion process before the Endcap. I look at prompt photons heading towards the Endcap from a MC gamma-jet sample with a partonic pT of 9-11 GeV. I identify those photons that convert using the GEANT record. The top left plot shows the total number of direct photons and those that convert. I register a 16% conversion rate. This is consistent with Jason's 2006 SVT review. The top right plot shows the source of conversion, where most of the conversions emanate from the SVT support cone, also consistent with Jason's study. The bottom left plot shows the separation in the SMD between the projected location of the photon and the location of the electron/positron from conversion.

Shower shapes comparison

This

PDF

file shows several shower shapes in a single plot for comparison:

MC - Monte Carlo shower shape from the 9-11 GeV pT gamma-jet Pythia sample
DD - Data-driven Monte Carlo shower shape (Each final state photon shower shape is replaced with a corresponding shower shape from data in the same sector configuration, energy, preshower, and U/V-plane bin).
Standard MC - Monte Carlo shower shape parametrized by Hal (also from the 9-11 GeV pT gamma-jet Pythia sample)
Will - Data shower shape derived from photons from eta decays by Will using a modified version of Weihong/Jason meson pi0 finder
Pibero - Data shower shape derived from photons from eta decays by Pibero using a crude eta finder

Shower Shapes Sorted by SMD Plane, Sector Configuration, Energy and Preshower

These Shower Shapes are binned by:

SMD plane (U and V)
Sector configuration with the formula sector%3 where sector=1..12, so 3 different bins. More details can be found at the EEMC Web site under the Geometry link.
Energy of the photon (E < 8 GeV and E > 8 GeV)
Preshower energy (pre1==0&&pre2==0) and (pre1>0||pre2>0)

They are then fitted with a triple-Gaussian of the form:

[0]*([2]*exp(-0.5*((x-[1])/[3])**2)/(sqrt(2*pi)*[3])+[4]*exp(-0.5*((x-[1])/[5])**2)/(sqrt(2*pi)*[5])+(1-[2]-[4])*exp(-0.5*((x-[1])/[6])**2)/(sqrt(2*pi)*[6]))

Comparison of Sided Residuals for Monte Carlo (MC) and Data-Driven (DD) Shower Shapes

All fits to MC are with reference to the old Monte carlo fit function:

[0]*(0.69*exp(-0.5*((x-[1])/0.87)**2)/(sqrt(2*pi)*0.87)+0.31*exp(-0.5*((x-[1])/3.3)**2)/(sqrt(2*pi)*3.3))

All fits to the data are with reference to a single

Shower Shape

. The fit function is:

[0]*([2]*exp(-0.5*((x-[1])/[3])**2)/(sqrt(2*pi)*[3])+[4]*exp(-0.5*((x-[1])/[5])**2)/(sqrt(2*pi)*[5])+(1-[2]-[4])*exp(-0.5*((x-[1])/[6])**2)/(sqrt(2*pi)*[6]))

Comparison of Sided Raw Tails for Monte Carlo (MC) and Data-Driven (DD) Shower Shapes

Pibero Djawotho

Last updated Tue Apr 8 17:29:40 EDT 2008

2008.04.12 Data-Driven Residuals

Data-Driven Residuals

Gammas

Jets

Background Rejection vs. Signal Efficiency

Partonic p_T=9-11 GeV	Partonic p_T=9-11 GeV

Background Rejection vs. Signal Efficiency (Neutral Meson pT > 8 GeV)

Pibero Djawotho

Last updated Sat Apr 12 13:27:50 EDT 2008

2008.04.12 Pythia Gamma-Jets

Pythia Gamma-Jets

Gamma-Jet Yields

During Run 6, the L2-gamma trigger (trigger id 137641) sampled 4717.10 nb^-1 of integrated luminosity. By restricting the jet to the Barrel, |η_jet|<1, and the gamma to the Endcap, 1<η_gamma<2, the yield of gamma-jets is estimated as the product of the luminosity, the cross section, and the fraction of events in the phasespace above. The total cross section reported by Pythia for gamma-jet processes at different partonic p_T thresholds is listed in the table below. No efficiencies are included.

p_T threshold [GeV]	Total cross section [mb]	Fraction	N_gamma-jets
5	6.551E-05	0.0992	30654
6	3.075E-05	0.1161	16840
7	1.567E-05	0.1150	8500
8	8.654E-06	0.1131	4617
9	4.971E-06	0.1223	2868
10	2.953E-06	0.1151	1603

Gamma-Jets p_T slope

The p_T slope is exp(-0.69*pT)=2^(-pT), so the statistics are halved with each 1 GeV increase in p_T.

References

Pibero Djawotho

Last updated Sat Apr 12 15:15:56 EDT 2008

2008.04.16 Jet Finder QA

Jet Finder QA

Pibero Djawotho

Last updated Wed Apr 16 08:33:01 EDT 2008

2008.04.20 BUR 2009

Partonic pT=7-9 GeV

Partonic pT=9-11 GeV

Combined Partonic pT

2008.04.22 Run 6 Photon Yield Per Trigger

Run 6 Photon Yield Per Trigger

Introduction

The purpose of this study is to estimate the photon yield per trigger in the Endcap Electromagnetic Calorimeter during Run 6. The trigger of interest is the L2-gamma trigger. Details of the STAR triggers during Run 6 were compiled in the 2006 p+p run (run 6) Trigger FAQ by Jamie Dunlop. The triggers relevant to this study are reproduced in the table below for convenience.

Trigger id	Trigger name	Description
117641	eemc-http-mb-l2gamma	EEMC HT > th1 (12, 2.6 GeV) and TP > TH1 (17, 3.8 GeV), minbias condition, L2 Gamma algorithm, reading out slow detectors, L2 thresholds at 2.9, 4.5
127641	eemc-http-mb-l2gamma	EEMC HT > th1 (12, 2.6 GeV, run < 7100052;13, 2.8 GeV, run >=7100052) and TP > TH1 (17, 3.8 GeV, run < 710052; 21, 4.7 GeV, run>=7100052 ), minbias condition, L2 Gamma algorithm, reading out slow detectors, L2 thresholds at 3.4, 5.4, transverse running
137641	eemc-http-mb-l2gamma	EEMC HT > th1 (16, 3.5 GeV) and TP > th1 (20, 4.5 GeV), minbias condition, L2 Gamma algorithm, reading out slow detectors, L2 thresholds at 3.7, 5.2, longitudinal running 2

The luminosity sampled by each trigger was also caclulated here by Jamie Dunlop. The luminosity for the relevant triggers is reproduced in the table below for convenience. The figure-of-merit (FOM) is calculated as FOM=Luminosity*P_B*P_Y for transverse runs and FOM=Luminosity*P_B²*P_Y² for longitudinal runs where P_B is the polarization of the blue beam and P_Y is the polarization of the yellow beam. Naturally, in spin physics, the FOM is the better indicator of statistical precisison.

Trigger	First run	Last run	Luminosity [nb^-1]	Figure-of-merit [nb^-1]
117641	7093102	7096017	118.88	11.89
127641	7097009	7129065	3219.04	1099.43
137641	7135050	7156028	4717.10	687.65

Event selection

Trigger selection

For this study, only the trigger of longitudinal running 2 (137641) is used. As mentioned above, at level-0, an EEMC high tower above 3.5 GeV and its associated trigger patch above 4.5 GeV in transverse energy coupled with a minimum bias condition, which is simply a BBC coincidence to ensure a valid collision, is required for the trigger to fire. The EEMC has trigger patches of variable sizes depending on their location in pseudorapidity. (The BEMC has trigger patches of fixed sizes, 4x4 towers.) At level-2, a high tower above 3.7 GeV and a 3x3 patch above 5.2 GeV in transverse energy is required to accept the event.

Gamma candidates

In addition to selecting events that were tagged online by the L2-gamma trigger, the offline

StGammaMaker

looks for tower clusters with minimum transverse energy of 5 GeV. These clusters along with their associated TPC tracks, preshower and postshower tiles, and SMD strips form gamma candidates. Gamma trees for the 2006 trigger ID 137641 with primary vertex are located at

/star/institutions/iucf/pibero/2006/gammaTrees/

Track isolation

The gamma candidate is required to have no track pointing to any of its towers.

EMC isolation

The gamma candidate is required to have 85% of the total transverse energy in a cone of radius 0.3 in eta-phi space around the position of the gamma candidate. That is E

_T^gamma

_T^cone

> 0.85 and R=√Δη

+Δφ

=0.3 is the cone radius.

Jet Reconstruction

The gamma candidate is matched to the best away-side jet with neutral fraction < 0.9 and cos(φ

_gamma

-φ

_jet

) < -0.8. The 2006 jet trees are produced by Murad Sarsour at PDSF in

/eliza13/starprod/jetTrees/2006/trees/

. A local mirror exists at RCF under the directory

/star/institutions/iucf/pibero/2006/jetTrees/

Spin Information

Jan Balewski has an excellent write-up, Offline spin DB at STAR, on how to get spin states. I obtain the spin states from the skim trees in the jet trees directory. In brief, the useful spin states are:

Blue Beam Polarization	Yellow Beam Polarization	Spin4
P	P	5
P	N	6
N	P	9
N	N	10

Event Summary

L2-gamma triggers	730128
Endcap gamma candidates	723848
Track isolation	246670
EMC isolation	225400
Away-side jet	99652
SMD max sided residual	19281
Barrel-only jet	15638

Note the number of L2-gamma triggers include only those events with a primary vertex and at least one gamma candidate (BEMC or EEMC).

Gamma-Jet Plots

Comparison of pT Slope with Pythia

Partonic Kinematics Reconstruction

Open Questions

I count ~2.4M events with trigger id 137641 using the Run 6 Browser, however my analysis only registers about ~0.78M.

Pibero Djawotho

Last updated Tue Apr 22 11:40:18 EDT 2008

2008.05.07 Number of Jets

Number of Jets

After selecting Endcap gamma candidates out of L2-gamma triggers, applying track and EMC isolation cuts, and matching the Endcap gamma candidate to an away-side jet, I record the number of jets below per event. Surprisingly, 8% of the events only have 1 jet. Those are events where the Endcap gamma candidate was not reconstructed as a neutral jet by the jet finder. The question is why.

I display both Barrel and Endcap calorimeter towers (the z-axis represents tower energy) and draw a circle of radius 0.3 around the gamma candidate and a circle of radius 0.7 around the away-side jet for 2006 pp200 run 7136022. Even though many of the gamma candidates not reconstructed by the jet finder are at the forward edge of the Endcap, it is not at all clear why those that are well within the detector are not being reconstructed.

Pibero Djawotho

Last updated Wed May 7 09:54:32 EDT 2008

2008.05.09 Gamma-jets pT distributions

Gamma-jets pT distributions

Note:

No cuts on residuals applied.

Not cut on number of towers in gamma cluster

Number of towers in gamma cluster <= 9

Number of towers in gamma cluster <= 4

Number of towers per cluster distributions

Gamma candidates xy-distribution

z-vertex distribution

Eta distribution

Phi distribution

log10(E_post/E_tow) distribution

pT asymmetry

References

Pibero Djawotho

Last updated Fri May 9 08:19:00 EDT 2008

2008.05.19 Binning the shower shape library

Binning the shower shape library

Distributions

Shower Shapes

Pibero Djawotho

Last updated Mon May 19 12:09:48 EDT 2008

2008.06.03 Jet A_LL Systematics

Jet A_LL Systematics

Hypernews discussion

jet A_LL systematic possibility

References

Pibero Djawotho

Last updated Tue Jun 3 15:35:24 EDT 2008

2008.06.18 Photon-jet reconstruction with the EEMC - Part 2 (STAR Collaboration Meeting - UC Davis)

2008.07.16 Extracting A_LL and DeltaG

Extracting A_LL and DeltaG

Determining state of beam polarization for Monte Carlo events

While Pythia does a pretty good job of simulating prompt photon production in p+p collisions, it does not include polarization for the colliding protons nor partons. A statistical method for assigning polarization states for each event based on A_LL [1] is demonstrated in this section. For an average number of interactions for each unpolarized bunch crossing, N_eff, the occurence of an event with a particular polarization state obeys a Poisson distribution with average yield of events per bunch crossing:

For simplicity, the polarizations of the blue and yellow beams are assumed to be P

=0.7 and N

_eff

=0.01. The "+" spin state defines the case where both beams have the same helicities and the "-" spin state for the case of opposite helicities. The asymmetry A

_LL

is calculated from the initial states polarized and unpolarized parton distribution functions and parton-level asymmetry:

The algorithm then consists in alternatively drawing a random value N

_int

from the Poisson distributions with mean μ

₊

and μ

until N

_int

>0 at which point an interaction has occured and the event is assigned the current spin state. The functioning of the algorithm is illustrated in Figure 1a where an input A

_LL

=0.2 was fixed and N

_trials

=500 different asymmetries were calculated. Each trial integrated N

_total

=300 events. It is then expected that the mean A

_LL

~0.2 and the statistical precision~0.1:

Indeed, both the A

_LL

and its error are reproduced. In addition, variations on the number of events per trial were investigated (N

_total

) in Figure 1b. The extracted width of the Gaussian distribution for A

_LL

is consistent with the prediction for the error (red curve).

ROOT macro used to generate Figure 1a SimALL.C
ROOT macro used to generate Figure 1b SimALL2.C

Figure 1a	Figure 1b

Event reconstruction

For this study, the gamma-jets Monte Carlo sample for all partonic p_T were used. As an example, the prompt photon processes for the partonic p_T bin 9-11 GeV and their total cross sections are listed in the table below. Each partonic p_T bin was divided into 15 files each of 2000 events.

 ==============================================================================
 I                                  I                            I            I
 I            Subprocess            I      Number of points      I    Sigma   I
 I                                  I                            I            I
 I----------------------------------I----------------------------I    (mb)    I
 I                                  I                            I            I
 I N:o Type                         I    Generated         Tried I            I
 I                                  I                            I            I
 ==============================================================================
 I                                  I                            I            I
 I   0 All included subprocesses    I         2000          9365 I  3.074E-06 I
 I  14 f + fbar -> g + gamma        I          331          1337 I  4.930E-07 I
 I  18 f + fbar -> gamma + gamma    I            2             8 I  1.941E-09 I
 I  29 f + g -> f + gamma           I         1667          8019 I  2.579E-06 I
 I 114 g + g -> gamma + gamma       I            0             1 I  1.191E-10 I
 I 115 g + g -> g + gamma           I            0             0 I  0.000E+00 I
 I                                  I                            I            I
 ==============================================================================

The cross sections for the different partonic p_T bins has been tabulated by Michael Betancourt and is reproduced here for convenience.

Partonic p_T [GeV]	Cross Section [mb]
3-4	0.0002962
4-5	0.0000891
5-7	0.0000494
7-9	0.0000110
9-11	0.00000314
11-15	0.00000149
15-25	0.000000317
25-35	0.00000000990
35-45	0.000000000449

These events were processed through the 2006 pp200 analysis chain, albeit without any cuts on the SMD. The simulated quantities were taken from the Pythia record and the reconstructed ones from the analysis.

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Partonic kinematics reconstruction

PartonicKinematics.C

Figure 10

Figure 11

Figure 12

Figure 13

Figure 14

Figure 15 (a)

Figure 15 (b): quark from proton 1 (blue beam, +z direction)

Figure 15 (c): quark from proton 1 (blue beam, +z direction) and q + g -> q + gamma subprocess (29)

Figure 15 (d): quark from proton 1 (blue beam, +z direction) and q + qbar -> gamma + g subprocess (14)

Predictions for A_LL and direct determination of DeltaG(x)

DeltaG.C

Figure 16 (a)

Figure 16 (b): quark from proton 1 (blue beam, +z direction)

Figure 16 (c): quark from proton 1 (blue beam, +z direction) and q + g -> q + gamma subprocess (29)

Figure 16 (d): quark from proton 1 (blue beam, +z direction) and q + qbar -> gamma + g subprocess (14)

References

Pibero Djawotho

Last updated Wed Jul 16 10:29:22 EDT 2008

2008.07.20 How to install Pythia 6 and 8 on your laptop?

How to install Pythia 6 and 8 on your laptop?

Install Pythia 6 and build the interface to ROOT

pythia6.tar.gz

ftp://root.cern.ch/root/pythia6.tar.gz

tar zxvf pythia6.tar.gz

pythia6/

cd pythia6/
./makePythia6.linux

Installing ROOT from Source

Pythia Event Generators

Install Pythia 8

http://home.thep.lu.se/~torbjorn/Pythia.html

tar zxvf pythia8108.tgz

pythia8108/

README

PYTHIA8

PYTHIA8DATA

/etc/profile.d/pythia8.sh

export PYTHIA8=$HOME/pythia8108
export PYTHIA8DATA=$PYTHIA8/xmldoc

./configure --enable-shared
make

Install ROOT from source

http://root.cern.ch/

tar zxvf root_v5.20.00.source.tar.gz
cd root/
./configure linux --with-pythia6-libdir=$HOME/pythia6 \
  --enable-pythia8 \
  --with-pythia8-incdir=$PYTHIA8/include \
  --with-pythia8-libdir=$PYTHIA8/lib
make
make install

/etc/profile.d/root.sh

export ROOTSYS=/usr/local/root
export PATH=$PATH:$ROOTSYS/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$ROOTSYS/lib:/usr/local/pythia6
export MANPATH=$MANPATH:$ROOTSYS/man

root $ROOTSYS/tutorial/pythia/pythiaExample.C
root $ROOTSYS/tutorial/pythia/pythia8.C

Pibero Djawotho

Last updated on Sun Jul 20 23:35:39 EDT 2008

2008.07.23 Hot Strips Identified by Hal Spinka

Hot Strips Identified by Hal Spinka

Run 7136034 Sector 8

Strips 08U020, 08U080, 08V185 and 08V225

Run 7137036 Sector 9

Strips 09V064

Pibero Djawotho

Last updated Wed Jul 23 03:40:54 EDT 2008

2008.07.24 Strips from Weihong's 2006 ppLong 20 runs

Strips from Weihong's 2006 ppLong 20 runs

Energy [GeV] vs. strip id

2006ppLongRuns.pdf

Raw ADC vs. strip id

7136022.pdf 7136033.pdf 7136034.pdf 7137036.pdf 7138001.pdf 7138010.pdf 7138032.pdf 7140046.pdf 7143012.pdf 7144014.pdf 7145018.pdf 7145024.pdf 7146020.pdf 7146077.pdf 7147052.pdf 7148027.pdf 7149005.pdf 7152062.pdf 7153008.pdf 7155052.pdf

Pibero Djawotho

Last updated Thu Jul 24 10:35:50 EDT 2008

G/h Discrimination Algorithm (Willie)

My blog pages, from first to last:

01/25: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/jan/25/photon-analysis-progress-week-1-21-08-1-25-08. This post discusses the problem with the spike in secondary tracks at eta=1 in our single-particle simulations.

01/28: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/jan/28/further-qa-plots. This post has QA plots for every particle sample Ross generated, both in the barrel and in the endcap.

02/01: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/01/more-qa-plots-time-efficiencies. This post has QA plots for gamma and piminus (barrel and endcap) as well as reconstruction efficiencies.

02/04: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/04/photon-qa-efficiency-plots-error-bars. This post adds error bars to the reconstruction efficiencies for the photon barrel sample.

02/05: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/05/first-clustering-plots. This post has the first clustering plots, for muons and gammas (barrel only), showing cluster energy, energy-weighted cluster eta and phi, and the number of seeds and clusters passing the thresholds for each event.

02/12: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/12/preshower-plots. This post has preshower plots from the gamma barrel sample, but the plots are of all preshowers in the event and use the preshower information generated by the BEMC simulator and so are not useful.

02/13: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/13/more-clustering-plots. This post has geant QA plots combined with the clustering plots from 02/05 above, but for the gamma and piminus barrel samples.

02/19: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/19/cluster-track-matching-plots. This post investigates the cluster-to-track matching for the gamma barrel sample, using a simple distance variable d=sqrt((delta eta)^2+(delta phi)^2)) to match clusters to tracks and plotting the resulting energy distributions, the energy ratio, etc.

02/21: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/21/more-preshower-plots. This post plots preshower distributions but uses the preshower information from the BEMC simulator and so is not useful.

02/28: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/28/further-preshower-plots-not-completed-yet. Figures 1, 3, and 5 in this post plot the geant preshower energy deposition for gammas, piminuses, and muons (Figs 2, 4, and 6 plot reconstructed preshower information again and so are not useful).

03/04: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/mar/04/muon-preshower-plots. This post expands on the post of 02/28, with additional plots using the geant preshower information, including preshower cluster energy vs. tower cluster energy.

03/06: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/mar/06/first-physics-cuts. This post basically recaps the previous post and adds a cut: unfortunately the cut is based partly on the thrown particle energy and so is not useful.

03/18: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/mar/18/smd-qa-plots. This post plots energy-weighted SMD phi and eta distributions, as well as the total energy deposited in the BSMDE and BSMDP strips located behind a cluster.

03/28: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/mar/28/smd-clustering-plots. This post contains SMD clustering plots for barrel gamma and piminus samples.

Neutral Pions 2005: Frank Simon

Information about the 2005 Spin analysis (focused on A_LL and <z>, some QA plots for cross section comparisons) will be archived here. The goal is obviously the 2005 Pi0 spin paper.

Invariant Mass and Width: Data-MC

Here I show the invariant masses and corresponding widths I obtain using my cross section binning. These are compared to MC values.

The Method:

Invariant mass Data histograms (low mass background and combinatoric background subtracted) are fitted with a gaussian in the range 0.1 - 0.18 GeV/c^2. This gives the mass (gaussian mean) and width (gaussian sigma)
MC invariant mass histograms are obtained from correctly associated MC Pi0s after reconstruction. No weighting of the different partonic pt samples is performed. This can (and will) introduce a bias
- Then the same fitting procedure as for data is applied

The results are shown in the two figures below.

Mass:

Width:

Neutral Pion Paper: 2005 ALL & <z>

Neutral Pion Paper for 2005 data: Final Results.

There are two spin plots planned for the paper, one with the 2005 A_LL and one with the <z>. In addition to this the cross section will be included (analysis by Oleksandr used for publication).

Final result for A_LL:

Figure 1: Double longitudinal spin asymmetry for inclusive Pi0 production. The curves show predictions from NLO pQCD calculations based on the gluon distributions from the GRSV, GS-C and DSSV global analyses. The systematic error shown by the gray band does not include a 9.4% normalization uncertainty due to the polarization measurement.

The chi2/ndf for the different model curves are:

GRSV Std: 0.740636
GRSV Max: 3.49163
GRSV Min: 0.94873
GRSV Zero: 0.546512
GSC: 0.513751
DSSV: 0.543775

Final Result for <z>:

Figure 2: Mean momentum fraction of Pi0s in their associated jet as a function of p_T for electromagnetically triggered events. The data points are plotted at the bin center in pion p_T and are not corrected for acceptance or trigger effects. Systematic errors, estimated from a variation of the cuts, are shown by the grey band underneath the data points. The lines are results from simulations with the PYTHIA event generator. The solid line includes detector effects simulated by GEANT, while the dotted line uses jet finding on the PYTHIA particle level. The inset shows the distribution of p_{T, π} / p_{T, Jet} for one of the bins, together with a comparison to PYTHIA with a full detector response simulation.

Below are links to details about the two results.

<z> Details

The goal of this analysis is to relate the neutral pions to the jets they are embedded in. The analysis is done using the common spin analysis trees, which provide the necessary tools to combine the jet and neutral pion analysis.

A neutral pion is associated to a parent jet if it is within the jet cone of 0.4 in eta and phi. To avoid edge effects in the detector, only neutral pions with 0.3 < eta < 0.7 are accepted.

Cut details:

E_neutral / E_total < 0.95

higher energy photon of Pi0 > 2.8 GeV (HT1 trigger); > 3.6 GeV (HT2 trigger)

combination HT1/HT2: below 5.7 GeV only HT1 is used, above that both HT1 and HT2 are accepted

The final result uses both HT1 and HT2 triggers, but a trigger separated study has also been done, as shown below. There, HT2 includes only those HT2 triggers that do not satisfy HT1 (because of prescale).

Figure 1: <z> for Pi0 in jets as a function of p_T for HT1 and HT2 triggers. Also shown is the mean jet p_T as a function of pion p_T.

Bin-by-Bin momentum ratio

Figure 2: Bin-by-bin ratio of pion to jet p_T. The <z> is taken from the mean of these distributions, the error is the error on the mean. A small fraction of all entries have higher Pi0 p_T than jet p_T. Similar behavior is also observed for Pythia MC with GEANT jets. This obviously increases the <z>. An alternative would be to reject those events. The agreement with MC becomes worse if this is done.

Here is the data - MC comparison for 3 of the above bins. For the simulation, the reconstruction of the Pi0 is not required to keep statistics reasonable, so the true Pi0 pt is used. However, the MC jet finding uses all momenta after Geant, this is why the edges are "smoother" in the MC plot than in the data plots. Since <z> is an average value, this is not expected to be affected by this, since on average the Pi0 pt is reconstructed right.

Figure 3: Data / MC for Bin 5: 6.7 to 8 GeV

Figure 4: Data / MC for Bin 6: 8 to 10 GeV

Figure 5: Data / MC for Bin 7: 10 to 12 GeV

A_LL Details

Details on the A_LL result and the systematic studies:

The result in numbers:

Bin	<p_T> [GeV] in bin	A_LL	stat. error	syst. error
1	4.17	0.01829	0.03358	0.01603
2	5.41	-0.01913	0.02310	0.01114
3	7.06	0.00915	0.03436	0.01343
4	9.22	-0.06381	0.06366	0.01862

A_LL as separated by trigger:

Figure 1: A_LL as a function of p_T for HT1 (black) and HT2 (red) triggers separately. HT1 here is taken as all triggers that satisfy the HT1 requirement, but not HT2. Since the HT2 prescale is one, there are very little statistics for HT1 at the highest p_T. The highest p_T point for HT1 is outside the range of the plot, and has a large error bar. The high p_T HT1 data is used in the combined result.

Systematics: Summary

	Bin 1	Bin 2	Bin 3	Bin4
relative luminosity	0.0009	0.0009	0.0009	0.0009
non-longitudinal pol.	0.0003	0.0003	0.0003	0.0003
beam background	0.0012	0.0084	0.0040	0.0093
yield extraction	0.0144	0.0044	0.0102	0.0116
invariant mass background	0.0077	0.0061	0.0080	0.0108
total	0.01603	0.01114	0.01343	0.01862

The first two systematics are common to all spin analyses. The numbers here are taken from the jet analysis. No Pi0 non-longitudinal analysis has been performed due to lacking statistics. These systematics are irrelevant compared to the others.

The analysis specific systematics are determined from the data, and as such are limited by statistics. The real systematic limit of a Pi0 analysis with a very large data sit will be much lower.

For the yield extraction systematic the invariant mass cuts for the pion yield extraction are varied. The systematic is derived from the maximum change in asymmetry with changing cuts.

For the beam background, the systematic is derived by studying how much A_LL changes when the beam background is removed. This is a conservative estimate that covers the scenario that only half of the background is actually removed. The asymmetry of the background events is consistent with zero.

For the invariant mass background systematic, A_LL is extracted in three invariant mass bins outside the signal region. The amount of background under the invariant mass peak (includes combinatorics, low mass and others) is estimated from the invariant mass distribution as shown below. For all three bins, the background A_LL is consistent with zero, a "worst case" of value + 1 sigma is assumed as deviation from the signal A_LL.

Invariant mass distribution:

Figure 2: Invariant mass distribution for HT1 events, second p_T bin. The red lines are the MC expectations for Pi0 and Eta, the green line is low mass background, the magenta line is combinatoric background, the thick blue line is a pol2 expectation for the other background, the blue thinner line is the total enveloppe of all contributions, compared to the data. At low mass, the background is overestimated.

Other systematic studies: False Asymmetries

False asymmetries (parity-violating single spin asymmetries) were studied to exclude systematic problems with spin asignments and the like. Of course the absence of problems in the jet analysis with the same data set makes any issues very unlikely, since jet statistics allow much better verifications than Pi0s. Still, single spin asymmetries were studied, and no significant asymmetries were observed. For both triggers, both asymmetries (yellow and blue) and for all p_T bins the asymmetries are consistent with zero, in most cases within one sigma of zero. So there are no indications for systematic effects. The single spin asymmetries are shown below:

Figure 3: Single spin asymmetry epsilon_L for the blue beam.

Figure 4: Single spin asymmetry epsilon_L for the yellow beam.

Neutral strange particle transverse asymmetries (tpb)

Neutral strange particle transverse asymmetry analysis

Here is information regarding my analysis of transverse asymmetries in neutral strange particles using 2006 p + p TPC data. This follows-on from and expands upon the earlier analysis I did, which can still be found at star.bnl.gov/protected/strange/tpb/analysis/. Comments, questions, things-you'd-like-to-see-done and so forth are welcomed. I'll catalogue updates in my blog as I make them.

The links listed below are in 'analysis-order'; best to use these for navigation rather than the alphabetically listed links Drupal links below/in the sidebar.

e-mail me at tpb@np.ph.bham.ac.uk

Data used

Data used in analysis

Data used for this analysis is 2006 p+p 200 GeV data taken with transverse polarisation, trigger setup "ppProductionTrans". This spanned days 97 (7th April) to 129 (9th May) inclusive. Trigger bemc-jp0-etot-mb-l2jet (ID 127622) is used. A file catalogue query with the following conditions gives a list of runs for which data is available:

trgsetupname=ppProductionTrans,tpc=1,year=2006,sanity=1,collision=pp200,
magscale=FullField,filename~physics,library=SL06e,production=P06ie

This generates a list of 549 runs. These runs are then compared against the spin PWG run QC (see http://www.star.bnl.gov/protected/spin/sowinski/runQC_2006) and are rejected if any of the following conditions are true:

The run is marked as unusable
The run has a jet patch trigger problem
The run has a spin bits problem
The run is unchecked

This excludes 172 runs, leaving 377 runs to be analysed.

I use a Maker class to create TTrees of event objects with V0 and spin information for these runs. Code for the Maker and Event classes can be found at /star/u/tpb/StRoot/StTSAEventMaker/ and /star/u/tpb/StRoot/StV0NanoDst/ respectively. Events are accepted only if they fulfill the following criteria:

Event contains specified trigger ID
StSpinDbMaker::isMaskedUsingBx48() returns false
StSpinDbMaker::offsetBX48minusBX7() returns zero

TTrees are produced for 358 runs (19 produce no/empty output), yielding 2,743,396 events.

The vertex distribution of events from each run are then checked by spin bits. A Kolmogorov test (using ROOT TH1::KolmogorovTest) is used to compare the vertex distributions for (4-bit) spin bits values 5, 6, 9 and 10. If any of the distributions are inconsistent, the run is rejected. Each run's mean event vertex z position is then plotted. Figure 1 shows the distribution, fitted with a Gaussian. A 3σ cut is applied and outlier runs rejected. 38 runs are rejected by these further cuts. The remaining 320 runs, spanning 33 RHIC fills and comprising 2,500,421 events, are used in the analysis.

Run-wise mean event vertex z distribution. It is well fitted by a Gaussian distribution.

Figure 1: Mean event vertex z for each run. The red lines indicate the 3σ cut.

Double spin asymmetry

I measure a double spin asymmetry defined as follows

A_TT=[N(parallel)-N(antiparallel)]/[N(parallel)+N(antiparallel)]

Equation 1

where N-(anti)parallel indicates yields measured in one half of the detector when the beam polarisations are aligned (opposite) and P₁ and P₂ are the polarisations of the beams. Accounting for the relative luminosity, these yields are given by

Equation 2

N(antiparallel)=N(upDown)/R5+N(downUp)/R6

Equation 2

where the arrows again indicate beam polarisations. Figures one and two show the fill-by-fill measurement of A_TT, corrected by the beam polarisation, summed over all p_T.

$K0s A_{TT}$

Figure 1: K⁰_S A_TT fill-by-fill

$Λ A_{TT}$

Figure 2: Λ A_TT fill-by-fill

Energy loss identification

Energy loss particle identification

The Bethe-Bloch equation can be used to predict charged particle energy loss. Hans Bichsel's model adds to this and the Bichsel function predictions for particle energy loss are compared with measured values. Tracks with dE/dx sufficiently far from the predicted value are rejected. e.g. when selecting for Λ hyperons, the positive track is required to have dE/dx consistent with that of a proton, and the negative track consistent with that of a π-minus.

The quantity σ = sqrt(N) x log( measured dE/dx - model dE/dx ) / R is used to quantify the deviation of the measured dE/dx from the model value. N is the number of track hits used in dE/dx determination and R is a resolution factor. A cut of |σ| < 3 applied to both V0 daughter tracks was found to significantly reduce the background with no loss of signal. Figures one to three below show the invariant mass distriubtions of the V0 candidates accepted and rejected and table one summarises the results of the cut. Background rejection is more successful for (anti-)Λ than for K⁰_S because most background tracks are pions; the selection of an (anti-)proton daughter rejects the majority of the background tracks.

Invariant mass spectrum of V0 candidates passing K0s dE/dx cut

Figure 1a: Invariant mass spectrum of V0 candidates under K0s hypothesis passing dE/dx cut

Invariant mass spectrum of V0 candidates failing K0s dE/dx cut

Figure 1b: Invariant mass spectrum of V0 candidates under K0s hypothesis failing dE/dx cut

Invariant mass spectrum of V0 candidates passing Lambda dE/dx cut

Figure 2a: Invariant mass spectrum of V0 candidates under Λ hypothesis passing dE/dx cut

Invariant mass spectrum of V0 candidates failing Lambda dE/dx cut

Figure 2b: Invariant mass spectrum of V0 candidates under Λ hypothesis failing dE/dx cut

Invariant mass spectrum of V0 candidates passing anti-Lambda dE/dx cut

Figure 3a: Invariant mass spectrum of V0 candidates under anti-Λ hypothesis passing dE/dx cut

Invariant mass spectrum of V0 candidates failing anti-Lambda dE/dx cut

Figure 3b: Invariant mass spectrum of V0 candidates under anti-Λ hypothesis failing dE/dx cut

Species	Pass (millions)	Fail (millions)	% pass
K⁰_S	95.5	48.9	66.2 %
Λ	32.5	111.9	22.5 %
anti-Λ	11.8	132.5	8.2 %

Table 1

Geometrical cuts

Energy loss cuts are successful in eliminating a significant portion of the background, but further reduction is required to give a clear signal. In addition final yields are calculated by a bin counting method, which requires that the background around the signal peak has a straight line shape. Therefore additional cuts are placed on the V0 candidates based on the geometrical properties of the decay. There are five quantities on which I chose to cut:

Distance of closest approach (DCA) of the V0 candidate to the primary vertex: if the V0 candidate is a genuine particle, its momentum vector should track back to the interaction point. Spurious candidates will not necessarily do so, therefore an upper limit is placed on the approach distance of the V0 to the interaction point.
DCA between the daughter tracks: due to detector resolution the daughter tracks never precisely meet, but placing an upper limit of the minimum distance of approach reduces background from spurious track crossings.
DCAs of the positive and negative daughter tracks to the primary vertex: the daughter tracks are curved due to the magnetic field and a neutral strange particle will decay some distance from the interaction point. Therefore the daughter tracks should not extrapolate back to the primary vertex, but to some distance away from it. Placing a lower limit on this distance can reduce background from tracks originating from the interaction point.
V0 decay distance: neutral strange particles decay weakly, with cτ ~ cm, so the decay vertex should typically be displaced from the interaction point. A lower limit placed on the decay distance of the V0 helps eliminate backgrounds from particles originating at the interaction point.

I wrote a class to help perform tuning of these geometrical cut quantities (see /star/u/tpb/StRoot/StV0CutTuning/) by a "brute force" approach; different permutations of the above quantities were attempted, and the resulting mass spectra analysed to see which permutations gave the best balance of background reduction and signal retention. In addition, the consistency of the background to a straight-line shape was required. Due to the limits on statistics, signal retention was considered a greater priority than background reduction. The cut values I decided upon are summarised in table one. Figures one to three show the resulting mass spectra (data are from all runs). Yields are calculated from the integral of bins in the signal (red) region minus the integrals of bins in the background (green) regions. Poisson (√N) errors are used. The background regions are fitted with a straight line, skipping the intervening bins. The signal to background quoted is the ratio of the maximum bin content to the value of the background fit evaluated at that mass. Note that the spectra have the the dE/dx cut included in addition to the geometrical cuts.

Species	Max DCA V0 to PV*	Max DCA between daughters	Min DCA + daughter to PV	Min DCA − daughter to PV	Min V0 decay distance
K⁰_S	1.0	1.2^**	0.5	0.0^**	2.0^**
Λ	1.5	1.0	0.0^**	0.0^**	3.0
anti-Λ	2.0^**	1.0	0.0^**	0.0^**	3.0

Table 1: Summary of geometical cuts. All cut values are in centimetres.

* primary vertex
** default cut present in micro-DST

Final K0s invariant mass specturm for all data with all cuts applied

Figure 1: Final K⁰_S mass spectrum with all cuts applied.

Final Lambda invariant mass specturm for all data with all cuts applied

Figure 2: Final Λ mass spectrum with all cuts applied.

Final anti-Lambda invariant mass specturm for all data with all cuts applied

Figure 3: Final anti-Λ mass spectrum with all cuts applied.

Single spin asymmetry using cross formula

Single Spin asymmetry using cross formula

Equation one shows the cross-formula used to calculate the single spin asymmetry.

AP=[sqrt(N(L,up)N(R,down))-sqrt(N(L,down)N(R,up))]/[sqrt(N(L,up)N(R,down))+sqrt(N(L,down)N(R,up))]

Equation 1

where N is a particle yield, L(eft) and R(ight) indicate the side of the polarised beam to which the particle is produced and arrows indicate the polarisation direction of the beam. Equation one cancels acceptance and beam luminosity and allows simply the raw yields to be used for the calculation. The asymmetry can be calculated twice; once for each beam, summing over the polarisation states of the other beam to leave it "unpolarised". I previously used only particles produced at forward η when calculating the blue beam asymmetry, and backward η for yellow, but I now sum over the full η range for each. Equations two and three give the numbers for up/down polarisation for blue (westward at STAR) and yellow (eastward) beams respectively in terms of the contributions from the four different beam polarisation permutations, and these permutations are related to spin bits numbers in table one.

N(blue,up)=N(upUp)+N(downUp),N(blue,down)=N(downDown)+N(upDown)

Equation 2

N(yellow,up)=N(upUp)+N(upDown),N(yellow,down)=N(downDown+N(downUp)

Equation 3

(in e.g. N(upUp), The first arrow refers to yellow beam polarisation, the second to blue beam.)

Beam polarisation		4-bit spin bits
Yellow	Blue	4-bit spin bits
Up	Up	5
Down	Up	6
Up	Down	9
Down	Down	10
Table 1

The raw asymmetry is calculated for each RHIC fill, then divided by the polarisation for that fill to give the physics asymmetry. Final polarisation numbers (released December 2007) are used. The error on the raw asymmetry is calculated by propagation of the √(N) errors calculated for each particle yield. The final asymmetry error incorporates the polarisation error (statistical and systematic errors summed in quadrature). The fill-by-fill asymmetries for each K⁰_S and Λ for each beam are shown in figures one and two. Anti-Λ results shall be forthcoming. An average asymmetry is calculated by performing a straight line χ² fit through the fill-by-fill values with ROOT. Table one summarises the asymmetry results. The asymmetry error is the error from the ROOT fit and is statistical only. All fits give a good χ² per degree of freedom and are consistent with zero within errors.

$Blue beam K0s A_{N}$

Figure 1a: K⁰_S blue beam asymmetry

$Yellow beam K0s A_{N}$

Figure 1b: K⁰_S yellow beam asymmetry

$Blue beam Λ A_{N}$

Figure 2a: Λ blue beam asymmetry

$Yellow beam Λ A_{N}$

Figure 2b: Λ yellow beam asymmetry

The above are summed over the entire p_T range available. I also divide the data into different transverse momentum bins and calculate the asymmetry as a function of p_T. Figures three and four show the p_T-dependent asymmetries. No p_T dependence is discernible.

$K^{0}_{S} blue beam A_{N} as a function of p_{T}$

Figure 3a: K⁰_S p_T-dependent blue beam A_N

$K^{0}_{S} yellow beam A_{N} as a function of p_{T}$

Figure 3b: K⁰_S p_T-dependent yellow beam A_N

$Λ blue beam A_{N} as a function of p_{T}$

Figure 4a: Λ p_T-dependent blue beam A_N

$Λ yellow beam A_{N} as a function of p_{T}$

Figure 4b: Λ p_T-dependent yellow beam A_N

Single spin asymmetry utilising relative luminosity

Single spin asymmetry making use of relative luminosity

I also calculate the asymmetry via an alternative method, making use of Tai Sakuma's relative luminosity work. The left-right asymmetry is defined as

Equation 1

where N_L is the particle yield to the left of the polarised beam. The decomposition of the up/down yields into contributions from the four different beam polarisation permutations is the same as given in the cross-asymmetry section (equations 2 and 3). Here, the yields must be scaled by the appropriate relative luminosity, giving the following relations:

Contributions to blue beam counts, scaled for luminosity

Equation 2

Contributions to yellow beam counts, scaled for luminosity

Equation 3

The relative luminosities R4, R5 and R6 are the ratios of luminosity for, respectively, up-up, up-down and down-up bunches to that for down-down bunches. I record the particle yields for each polarisation permutation (i.e. spin bits) on a run-by-run basis, scale each by the appropriate relative luminosity for that run, then combine yields from all the runs in a given fill to give fill-by-fill yields. These are then used to calculate a fill-by-fill raw asymmetry, which is scaled by the beam polarisation. The figures below show the resultant fill-by-fill asymmetry for each beam and particle species, summed over all p_T. The fits are again satisfactory, and give asymmetries consistent with zero within errors, as expected.

$K^{0}_{S} blue beam asymmetry using relative luminosity$

Figure 1a: Blue beam asymmetry for K⁰_S

$K^{0}_{S} yellow beam asymmetry using relative luminosity$

Figure 1b: Yellow beam asymmetry for K⁰_S

Lambda blue beam asymmetry using relative luminosity

Figure 2a: Blue beam asymmetry for Λ

Lambda yellow beam asymmetry using relative luminosity

Figure 2b: Yellow beam asymmetry for Λ

V0 decays

The appearance of the decay of an unobserved neutral strange particle into two observed charged daughter particles gives rise to the terminology 'V0' to describe the decay topology. The following neutral strange species have been analysed:

Species	Decay channel	Branching ratio
K⁰_S	π⁺ + π^-	0.692
Λ	p + π^-	0.639
anti-Λ	anti-p + π⁺	0.639

Candidate V0s are formed by combining together all possible pairs of opposite charge-sign tracks in an event. The invariant mass of the V0 candidate under different decay hypotheses can then be determined from the track momenta and the daughter masses (e.g. for Λ the positive daughter is assumed to be a proton, the negative daughter a π-minus). Raw invariant mass spectra are shown below. The spectra contain three contributions: real particles of the species of interest; neutral strange particles of a different species; combinatorial background from chance positive/negative track crossings.

Invariant mass spectrum for V0 candidates under K0s decay hypothesis

Figure 1: Invariant mass spectrum under K0s hypothesis

Invariant mass spectrum for V0 candidates under Lambda decay hypothesis

Figure 2: Invariant mass spectrum under Λ hypothesis

Invariant mass spectrum for V0 candidates under anti-Lambda decay hypothesis

Figure 3: Invariant mass spectrum under anti-Λ hypothesis

Selection cuts are applied to the candidates to suppress the background whilst maintaining as much signal as possible. There are two methods for reducing background; energy-loss particle identification and geometrical cuts on the V0 candidates.

placeholder

For later use.

Photon-jet with the Endcap (Ilya Selyuzhenkov)

Gamma-jets

Photon-jet reconstruction with the EEMC detector

W-analysis

Calorimeter studies for the W analysis

2008

Year 2008 posts

01 Jan

January 2008 posts

2008.01.30 Selecting gamma-jet candidates out of the jet trees

Ilya Selyuzhenkov January 30, 2008

Data set

jet trees by Murad Sarsour for pp2006 run, runId=7136022 (~60K events, no triggerId cuts yet)

Jets gross features

Figure 1: Distribution of number of jets per event. Same data on a log scale is here.
Figure 2: Distribution of electromagnetic energy (EM) fraction, R_EM, for di-jet events (number of jets/event = 2).
R_EM = [E_t(endcap)+E_t(barrel)]/E_t(total).
Black histogram is for R_EM1 = max(Ra, Rb), red is for R_EM2 = min(Ra, Rb).
Ra and Rb are EM fraction for jets in the di-jet event.
Same data on a log scale is here.

Gamma-jet isolation cuts list:

selecting di-jet events with one of the jet dominated by EM energy,
and another one with more hadronic energy:
R_EM1 >0.9 and R_EM2 < 0.9
selecting di-jet events with jets pointing opposite in azimuth:
cos(phi1 - phi2) < -0.9
requiring the number of associated charged tracks with a first jet (with maximum EM fraction) to be less than 2:
nChargeTracks1 < 2
requiring the number of fired EEMC towers associated with a first jet (with maximum EM fraction) to be 1 or 2:
0 < nEEMCtowers1 < 3

Applying gamma-jet isolation cuts

Figure 3: Distribution of eta vs number of EEMC towers for the first jet (with maximum EM fraction).
Cuts:1-3 applied (no 0 < nEEMCtowers1 < 3 cut).
Figure 4: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction)
vs transverse momentum, pt2, of the second jet.
Cuts:1-4 applied
Figure 5: Distribution of mean transverse momentum, < pt1 >, of the first jet (with maximum EM fraction)
vs transverse momentum, pt2, of the second jet.
Cuts:1-4 applied
Figure 6: Distribution of pseudorapidity, eta1, of the first jet (with maximum EM fraction)
vs pseudorapidity, eta2, of the second jet.
Cuts:1-4 applied
Figure 7: Distribution of azimuthal angle, phi1, of the first jet (with maximum EM fraction)
vs azimuthal angle, phi2, of the second jet.
Cuts:1-4 applied
Figure 8: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction)
vs transverse energy sum for the EEMC towers associated with this jet.
Cuts:1-4 applied
Figure 9: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction)
vs transverse momentum, pt2, of the second jet.
Cuts:1-4 + Et(EEMC) > 3.0 GeV

02 Feb

February 2008 posts

2008.02.13 Gamma-jet candidates: EEMC response

Ilya Selyuzhenkov February 13, 2008

Data sample

Gamma-jet selection cuts are discussed here. There are 278 candidates found for runId=7136022.
Transverse momentum distribution for the gamma-jet candidates can be found here.

Vertex z distribution for di-jet and gamma-jet events

Figure 1: Vertex z distribution.

Red line presents gamma-jet candidates (scaled by x50). Black is for all di-jet events.
Same data on a log scale is here.
Figure 2: Average vertex z as a function of transverse momentum of the fist jet (with a largest EM energy fraction).
Red is for gamma-jet candidates. Black is for all di-jet events.
Strong deviation from zero for gamma-jet candidates at pt < 5GeV?

EEMC response for the gamma-jet candidate

EEMC response event by event for all 278 gamma-jet candidate can be found in this pdf file.
Each page shows SMD/Tower energy distribution for a given event:

First row on each page shows SMD response
for the sector which has a maximum energy deposited in the EEMC Tower
(u-plane is on the left, v-plane is on the right).
In the left plot (u-plane energy distribution) numerical values for
pt of the first jet (with maximum EM fraction) and the second jet are given.
In addition, fit results assuming gamma (single Gaussian, red line) or
neutral pion (double Gaussian, blue line ~ red+green) hypotheses are given.
m_{gamma gamma} value (it is shown in the right plot for v-plane).
If m_{gamma gamma} value is negative, then the reconstruction procedure has failed
(for example, no uv-strips intersection found, or tower energy and uv-strips intersection point mismatch, etc).
EEMC response for these "bad" events can be found in this pdf file.

If reconstruction procedure succeded, then
m_{gamma gamma} gives reconstructed invariant mass assuming that two gammas hit the calorimeter.

Figure 3: invariant mass distribution (assuming pi0 hypothesis).

Note, that I'm still working on my fitting algorithm (which is not explained here),
and fit results and the invariant mass distribution will be updated.
It is also shown the ratio for each u/v plane
of the integrated single Gaussian fit (red line) to the total energy in the plane
(look for "gamma U/V " values on the right v-plane plot).
Second and third rows on each page show the energy deposition in the
tower, pre-shower1, pre-shower2, and post-shower as a function of eta:phi (etaBin:phiBin).
Last row shows the hit distribution in the SMD for all sectors
(u-plane on the left, v-plane of the right).

Playing with a different cuts

Trying to isolate the real gammas which hits the calorimeter,
I have sorted events into different subsets based on the following set of cuts:

EEMC gamma-jet cuts (energetic photon hits EEMC with pt similar or greater to that of the opposite jet)
if (invMass < 0) reject
if (jet2_pt > jet1_pt) reject
if (jet1_pt < 7) reject
if (minFraction < 0.75) reject
(minFraction = gamma U/V - is a fraction of the integrated single Gaussian peak to the total energy in the uv-plane)

Figure 4: Sample gamma-jet candidate EEMC response
(all gamma-jet candidates selected according to these conditions can be found in this pdf file):
EEMC pi0 cuts:
if (invMass < 0) reject
if (jet2_pt < jet1_pt) reject
if (jet2_pt < 7) reject
if (minFraction < 0.75) reject

Event by event EEMC response for pi0 (di-jet) candidates
selected according to these conditions can be found in this pdf file.

2008.02.20 Gamma-jet candidates: more statistics from jet-trees

Ilya Selyuzhenkov February 20, 2008

Short summary

After processing all available jet-trees for pp2006 (ppProductionLong),
and applying all "gamma-jet" cuts (which are described below):

there are 47K di-jet events selected
for pt1>7GeV there are 5,4K gamma-jet candidates (3,7K with an additional cut of pt1>pt2)
Figure 1: 2,4K events with both pt1, pt2 > 7GeV
721 candidates within a range of pt1>pt2 and both pt1, pt2 > 7 GeV

Data set

jet trees by Murad Sarsour for pp2006 run, number of runs processed: 323
4.7M di-jet events found (no triggerId cuts yet)

Di-jets gross features

Figure 2: Distribution of electromagnetic energy (EM) fraction, R_EM, for di-jet events (number of jets/event = 2).
R_EM = [E_t(endcap)+E_t(barrel)]/E_t(total).
Black histogram is for R_EM1 = max(Ra, Rb), red is for R_EM2 = min(Ra, Rb).
Ra and Rb are EM fraction for jets in the di-jet event.
Same data on a log scale is here.

Gamma-jet isolation cuts:

selecting di-jet events with one of the jet dominated by EM energy,
and another one with more hadronic energy:
R_EM1 >0.9 and R_EM2 < 0.9
selecting di-jet events with jets pointing opposite in azimuth:
cos(phi1 - phi2) < -0.9
requiring the number of associated charged tracks with a first jet (with maximum EM fraction) to be less than 2:
nChargeTracks1 < 2
requiring the number of fired EEMC towers associated with a first jet (with maximum EM fraction) to be 1 or 2:
0 < nEEMCtowers1 < 3

Applying gamma-jet isolation cuts

Figure 3: Distribution of eta vs number of EEMC towers for the first jet (with maximum EM fraction).
Cuts:1-3 applied (no 0 < nEEMCtowers1 < 3 cut).
Figure 4: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction)
vs transverse momentum, pt2, of the second jet.
Cuts:1-4 applied
Figure 5: Distribution of mean transverse momentum, < pt1 >, of the first jet (with maximum EM fraction)
vs transverse momentum, pt2, of the second jet.
Cuts:1-4 applied
Figure 6: Distribution of pseudorapidity, eta1, of the first jet (with maximum EM fraction)
vs pseudorapidity, eta2, of the second jet.
Cuts:1-4 applied
Figure 7: Distribution of azimuthal angle, phi1, of the first jet (with maximum EM fraction)
vs azimuthal angle, phi2, of the second jet.
Cuts:1-4 applied
Figure 8: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction)
vs transverse energy sum for the EEMC towers associated with this jet.
Cuts:1-4 applied
Figure 9: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction)
vs transverse momentum, pt2, of the second jet.
Cuts:1-4 + Et(EEMC) > 3.0 GeV

2008.02.27 Tower based clustering algorithm, and EEMC/BEMC candidates

Ilya Selyuzhenkov February 27, 2008

Gamma-jet candidates before applying clustering algorithm

Gamma-jet isolation cuts:

selecting di-jet events with the first jet dominated by EM energy,
and the second one with a large fraction of hadronic energy:
R_EM1 >0.9 and R_EM2 < 0.9
selecting di-jet events with jets pointing opposite in azimuth:
cos(phi1 - phi2) < -0.8
requiring no charge tracks associated with a first jet (jet with a maximum EM fraction):
nCharge1 = 0

Figure 1: Transverse momentum

Figure 2: Pseudorapidity

Figure 3: Azimuthal angle

Tower based clustering algorithm

for each gamma-jet candidate finding a tower with a maximum energy
associated with a jet1 (jet with a maximum EM fraction).
Calculating energy of the cluster by finding all adjacent towers and adding their energy together.
Implementing a cut based on cluster energy fraction, R_cluster, where
R_cluster is defined as a ratio of the cluster energy
to the total energy in the calorimeter associated with a jet1.
Note, that with a cut Ncharge1 =0, energy in the calorimeter is equal to the jet energy.

Distribution of cluster energy vs number of towers fired in EEMC/BEMC

Figure 4: R_cluster vs number of towers fired in EEMC (left) and BEMC (right). No pt cuts.

Figure 5: R_cluster vs number of towers fired in EEMC (left) and BEMC (right). Additional cut: pt1>7GeV

Figure 6: jet1 pseudorapidity vs number of towers fired in EEMC (left) and BEMC (right).

R_cluster>0.9 cut: EEMC vs BEMC gamma-jet candidates

EEMC candidates: nTowerFiredBEMC=0
BEMC candidates: nTowerFiredEEMC=0

Figure 7: Pseudorapidity (left EEMC, right BEMC candidates)

Figure 8: Azimuthal angle (left EEMC, right BEMC candidates)

Figure 9: Transverse momentum (left EEMC, right BEMC candidates)

Number of gamma-jet candidates with an addition pt cuts

Figure 10: Transverse momentum (left EEMC, right BEMC candidates): pt1>7GeV

Figure 11: Transverse momentum (left EEMC, right BEMC candidates): pt1>7 and pt2>7

03 Mar

March 2008 posts

2008.03.03 EEMC SMD: u/v-strip energy distribution

Ilya Selyuzhenkov March 03, 2008

Data set: ppLongitudinal, runId = 7136033.

Some observations/questions:

In general distributions look clean and good
Sectors 7 and 9 for v-plane and sector 7 for u-plane are noise.
Sector 9 has a hot strip (id ~ 120)
In sector 3, strips id=0-5 in v-plane are hot (see figure 2 right, bottom)
Sectors 2 and 8 in u-plane and sectors 3 and 9 in v-plane have missing strips id=283-288?
Strips 288 are always empty?

Figure 1:Average energy E in the strip vs sector and strip number (max < E > = 0.0027)
same figure on a log scale

Figure 2: Average energy E for E>0.02 (max < E > = 0.0682)
Same figure on a log scale

2008.03.12 Gamma-jet candidates: 2-gammas invariant mass and Eemc response

Ilya Selyuzhenkov March 12, 2008

Gamma-jet candidates: 2-gammas invariant

Note: Di-jet transverse momentum distribution for these candidates can be found on figure 11 at this page

Figure 1:Invariant mass distribution for gamma-jet candidates assuming pi0 (2-gammas) hypothesys

Figure 2:Invariant mass distribution for gamma-jet candidates assuming pi0 (2-gammas) hypothesys
with an additional SMD isolation cut: gammaFraction >0.75
GammaFraction is defined as ratio of the integral
other SMD strips for the first peak to the total energy in the sector

EEMC response for the gamma-jet candidates (gammaFraction >0.75)

pdf file (first 100 events) with event by event EEMC response for the candidates reconstructed into pion mass (gammaFraction >0.75)
pdf file with event by event EEMC response for the candidates not reconstructed into pion mass
(second peak not found), but has a first peak with gammaFraction >0.75.

2008.03.20 Sided residual and chi2 distribution for gamma-jet candidates

Ilya Selyuzhenkov March 20, 2008

Side residual (no pt cut on gamma jet-candidates)

The procedure to discriminate gamma candidate from pions (and other background)
based on the SMD response is described at Pibero's web page.

Figure 1: Fit integral vs maximum residual for gamma-jet candidates requesting
no energy deposited in the EEMC pre-shower 1 and 2
(within a 3x3 clusters around tower with a maximum energy).

Black line is defined from MC simulations (see Jason's simulation web page, or Pibero's page above).

Figure 2: Fit integral vs maximum residual for gamma-jet candidates requesting requesting
no energy deposited in pre-shower 1 cluster and
no energy deposited in post-shower cluster (this cut is not really essential in demonstrating the main idea)

Figure 3: Fit integral vs maximum residual for gamma-jet candidates requesting requesting
non-zero energy deposited in both clusters of pre-shower 1 and 2

Side residual: first and second jet pt are greater than 7GeV

Event by event EEMC response for gamma-jet candidates for the case of
no energy deposited in the EEMC pre-shower 1 and 2 can be found in this pdf file

Figure 4: Fit integral vs maximum residual for gamma-jet candidates requesting
no energy deposited in the EEMC pre-shower 1 and 2

Figure 5: Fit integral vs maximum residual for gamma-jet candidates requesting requesting
no energy deposited in pre-shower 1 cluster and
no energy deposited in post-shower cluster

Figure 6: Fit integral vs maximum residual for gamma-jet candidates requesting requesting
non-zero energy deposited in both clusters of pre-shower 1 and 2

Chi2 distribution for gamma-jet candidates

Monte Carlo shape

Event Monte Carlo shape allows to distinguish gammas from background by cutting at chi2/ndf < 0.5
(although the distribution looks wider than for the case of Will's shape).

Figure 7: Chi2/ndf for gamma-jet candidates using Monte Carlo shape requesting
no energy deposited in both clusters of pre-shower 1 and 2

Figure 8: Chi2/ndf for gamma-jet candidates using Monte Carlo shape requesting
non-zero energy deposited in both clusters of pre-shower 1 and 2

Will''s shape

Less clear where to cut on chi2?

Figure 9: Chi2/ndf for gamma-jet candidates using Monte Carlo shape requesting
no energy deposited in both clusters of pre-shower 1 and 2

Figure 10: Chi2/ndf for gamma-jet candidates using Monte Carlo shape requesting
non-zero energy deposited in both clusters of pre-shower 1 and 2

2008.03.26 Sided residual and chi2 distribution for gamma-jet candidates (pre1,2 sorted)

Ilya Selyuzhenkov March 26, 2008

gamma-jet candidates (no pt cut)

Definitions:

F_peak - integral for a fit within [-2,2] strips around SMD u/v peak
D_peak - integral over the data within [-2,2] strips around SMD u/v peak
D_tail^max (D_tail^min) - maximum (minimum) integral over the data tail within +-[3,30] strips from a SMD u/v peak
F_tail is the integral over the fit tail within [3,30] strips from a SMD u/v peak.
Maximum residual = D_tail^max - F_tail

All results are for combined distributions from u and v planes: ([u]+[v])/2
Gamma-jet isolation cuts described here
Additional quality cuts:

Matching between 3x3 tower cluster and u-v high strip intersection
At least 4 strips fired within [-2,2] strips from a peak

Figure 1: F_peak vs maximum residual
for various cuts on energy deposited in the EEMC pre-shower 1 and 2
(within a 3x3 clusters around tower with a maximum energy).

Figure 2: F_data vs D_tail^max
Note:This plot is fit independend (only the peak position is defined based on the fit)

Figure 3: F_data vs D_tail^max-D_tail^max

Figure 4: Gamma transverse momentum vs jet transverse momentum

gamma-jet candidates: pt > 7GeV

Figure 5: F_peak vs maximum residual
for various cuts on energy deposited in the EEMC pre-shower 1 and 2
(within a 3x3 clusters around tower with a maximum energy).

Figure 6: F_data vs D_tail^max
Note:This plot is fit independend (only the peak position is defined based on the fit)

Figure 7: F_data vs D_tail^max-D_tail^max

Figure 8: Gamma transverse momentum vs jet transverse momentum

gamma-jet candidates: eta, phi, and max[u,v] strip distributions (no pt cuts)

Figure 9: Gamma pseudorapidity vs jet pseudorapidity

Figure 10: Gamma azimuthal angle vs jet azimuthal angle
Note: for the case of Pre1>1 && Pre2==0 there is an enhancement around phi_gamma = 0?

Figure 11: maximum strip in v-plane vs maximum strip in u-plane

Chi2 distribution for gamma-jet candidates (no pt cuts)

Figure 12:Chi2/ndf for gamma-jet candidates using Monte Carlo shape (combined for [u+v]/2 plane )

Figure 13:Chi2/ndf for gamma-jet candidates (combined for [u+v]/2 plane ) using Will's shape

2008.03.28 EEMC SMD shapes: gamma's from gamma-jets (data), MC, and eta-meson analysis

Ilya Selyuzhenkov March 28, 2008

Some observations:

SMD data-driven shapes from different analysis are in a good agreement (Figure 1, upper left plot)
Overall MC shape is too narrow compared to the data shapes (Figure 1, upper left plot)
Shapes are similar with or without gamma-jet 7GeV pt cut (compare Figures 1 and 2),
what may indicate that shape is independent on energy (at least within our kinematic limits).
Data-driven and MC shapes are getting close to each other (Figure 4, upper left plot)
when requiring no energy above threshold in both preshower layers and
with suppressed contribution from pi0 background.
The latter is achieved by using the information on
reconstructed invariant mass of 2gamma candidates (compare Figure 3 and 4).
One interpretation of this can be that in Monte Carlo simulations
the contribution from the material in front of the detector is underestimated
Energy distribution for each strip in the SMD peak does not looks like a Gaussian (Figure 5),
what makes very difficult to interpret results obtained from chi2 analysis (Figure 6-8).
Triple Gaussian fit gives a better description of the data shapes,
compared to the double Gaussian function (compare red and black lines on Figure 1-4)

Figure 1: EEMC SMD shape comparison for various preshower cuts
(black points shows u-plane shape only, v-plane results can be found here)

Figure 2: EEMC SMD shape comparison for various preshower cuts with gamma-jet pt cut of 7GeV
(black points shows u-plane shape only, v-plane results can be found here)

Figure 3: Shapes with an additional cut on 2-gamma candidates within pi0 invariant mass range.
Sample invariant mass distribution using "simple" pi0 finder can be found here
(black points shows u-plane shape only, v-plane results can be found here)

Figure 4: Shapes for the candidates when "simple" pi0 finder failed to find a second peak
(black points shows u-plane shape only, v-plane results can be found here)

Figure 5: Strip by strip SMD energy distribution.
Only 12 strips from the right side of the maximum are shown.
Zero strip (first upper left plot) corresponds to the high strip in the shape
Note, that already at the 3rd strip from a peak,
RMS values are comparable to those for a mean, and for a higher strips numbers RMS starts to be bigger that mean.
(results for u-plane only, v-plane results can be found here)

Comparing chi2 distributions for gamma-jet candidates using MC, Will, and Pibero's shapes

Results for side residual (together with pt, eta, phi distributions) for gamma-jet candidates can be found at this web page

Red histograms on Figures 6-8 shows chi2 distribution from MC-photons (normalized at chi2=1.4)
Blue histograms on Figures 6-8 shows chi2 distribution from MC-pions (normalized at chi2=1.4)

Figure 6: Chi2/ndf for gamma-jet candidates using Monte Carlo shape

Figure 7: Chi2/ndf for gamma-jet candidates using Will's shape (derived from eta candidates based on Weihong's pi0-finder)

Figure 8: Chi2/ndf for gamma-jet candidates using Pibero's shape (derived from eta candidates)

04 Apr

April 2008 posts

2008.04.02 EEMC SMD shapes: data-driven (eta, gamma-jet) vs Monte Carlo (single gamma, gamma-jet)

Ilya Selyuzhenkov April 02, 2008

Some observations:

SMD data-driven shapes from eta-meson and gamma-jet studies
are in a good agreement for different preshower conditions
(compage Fig.1 green circles/triangles in upper-left/bottom-right plots)
single gamma MC shapes show preshower dependance,
but they are still narrower compared to the data shapes
(compare Fig.1 green circles vs blue open squares)
MC shapes for gamma-jet and single gamma are consistent (Fig.1, bottom right plot)

Figure 1: EEMC SMD shape comparison for various preshower cuts
Note:Only MC gamma-jet shape (open red squares) is the same on all plots

2008.04.02 Sided residual: Using data driven gamma-jet shape (3 gaussian fit)

Ilya Selyuzhenkov April 02, 2008

Figure 1: Side residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2
No EEMC SMD based cuts

Figure 2: Side residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2
"Simple" pi0 finder can not find a second peak

Figure 3: Side residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2
"Simple" pi0 finder reconstruct the invarian mass within [0.1,0.18] range

Figure 4: Side residual distribution (Projection for side residual in Figs.1-3 on vertical axis)

Figure 5: Signal (green: m < 0) vs background (black, red) separation

2008.04.02 Sided residual: single gamma Monte-Carlo simulations

Ilya Selyuzhenkov April 02, 2008

Side residual: single gamma Monte-Carlo simulations

Figure 1: Side residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2
No EEMC SMD based cuts

2008.04.03 chi2-shape subtraction for different Preshower conditions

Ilya Selyuzhenkov April 03, 2008

Request from Hal Spinka:

Hi Ilya,

I think you gave up on the chi-squared method too quickly, and am sorry I missed the phone meeting last week. So, I would like to make a request that will hopefully take a minimal amount of your time to show that all is okay. Then, if there is a delay in getting the sided residual information out and into the beam use request, you can still fall back on the chi-squared method.

In your March 28 posting, Figure 8 at the bottom, I would like to get numerical values for the events per bin for the black curves. I won't use the preshower1>0 and preshower2=0 data, so those you don't need to send. Also, I won't use the red or blue curve information.

I think your problem has been that you normalized your curves at chi-squared/ndf = 1.4 instead of the peak. What I plan to do is to normalize the (pre1=0, pre2=0) to the (pre1=0, pre2>0) data in the peak and subtract. The (pre1=0, pre2=0) set should have some single photons, but also some multiple photons. The (pre1=0, pre2>0) should also have single photons, and more multiple photons, since the chance that one of them will convert is larger. The difference should look roughly like your blue curve, though perhaps not exactly if Pibero's mean shower shape is not perfect (which it isn't). I will do the same thing with taking the difference between (pre1>0, pre2>0) and (pre1=0, pre2=0), and again the difference should look roughly like your blue curve. The (pre1>0, pre2>0) data should have even larger fraction of multiple photons than either of the other two data sets. I would expect the two difference curves to look approximately the same.

Hope this is possible for you to do. Since our reduced chi-squared curve looks so much like the one from CDF, I am pretty confident that we are okay, but this should be checked to convince people that we are not doing anything terribly wrong.

Reply by Ilya:

Dear Hal,

I have tried to implement your idea and produce a figure attached.

There are 4 plots in it:

1. Upper left plot shows normalized to unity (at maximum) chi2 distribution (obtained with Pibero shape for gamma-jet candidates) for a different pre1, pre2 conditions

2. Upper right plot shows bin-by-bin difference: a) between normalized chi2 for pre1=0, pre2>0 and pre1=0, pre2=0 (red) and b) between normalized chi2 for pre1>0, pre2>0 and pre1=0, pre2=0 (blue)

3. Bottom left Same as upper right, but normalization were done based on the integral within [-4,4] bins around maximum.

4. Bottom right Same as for upper right, but with a different normalization ([-4,4] bins around maximum)

I have also tried to normalized by the total integral, but the results looks similar.

Figure 1: See description above

Figure 2: Same without log scale (See description above)

2008.04.09 Applying gamma-jet reconstruction algorithm for gamma-jet simulated events

Ilya Selyuzhenkov April 09, 2008

Data sample:
Monte-Carlo gamma-jet sample for partonic pt range of 5-7, 7-9, 9-11,11-15, 15-25, 25-35 GeV.

Analysis: Simulated MuDst files were first processed through jet finder algorithm (thanks to Renee Fatemi),
and later analyzed by applying gamma-jet isolation cuts (see this link for details) and studying EEMC SMD response (see below).
To test the algorithm, Geant records were not used in this analysis.
Further studies based on Geant records (yield estimates, etc) are ongoing.

EESMD shapes comparison

Figure 1:Comparison between shower shape profile for data and MC.
Black circles shows results for MC gamma-jet sample (all partonic pt).
For v-plane results see this figure

Correlation between gamma and jet pt, eta, phi

Figure 2:Gamma vs jet transverse momentum.

Figure 3:Gamma vs jet azimuthal angle.

Figure 4:Gamma vs jet pseudo-rapidity.

Results from maximum sided residua study

Definitions for F_peak, D_peak, D_tail^max (D_tail^min) can be found here

Figure 5:F_peak vs maximum residual
for various cuts on energy deposited in the EEMC pre-shower 1 and 2
(within a 3x3 clusters around tower with a maximum energy).
Shower shape used to fit data is fixed to the shape from the previous gamma-jet study of real events
(see black point on Fig.1 [upper left plot] at this page)

Figure 6: F_peak vs D_tail^max: click here
Figure 7: F_peak vs D_tail^max-D_tail^min: click here

Postshower to SMD[uv] energy ratio

Figure 8:Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes

Figure 8a:
Same as figure 8, but for gamma-jet candidates from the real data (no pt cuts).
Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes

Figure 8b:
Comparison between gamma-jet candidates from data with different preshower conditions.
Points are normalized in peak to the case of pre1 > 0, pre2 > 0
Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes

Figure 8c:
Comparison between gamma-jet candidates from Monte-Carlo simulations with different preshower conditions.
Points are normalized in peak to the case of pre1 > 0, pre2 > 0
Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes

Additional QA plots

Figure 9: Jet neutral energy fraction
Figure 10: High v-strip vs u-strip
Figure 11: energy post shower (3x3 cluster)
Figure 12: Peak energy SMD-u
Figure 13: Peak energy SMD-v
Figure 14: Gamma phi
Figure 15: Gamma pt
Figure 16: Gamma eta
Figure 17: Delta gamma-jet pt
Figure 18: Delta gamma-jet eta
Figure 19: Delta gamma-jet phi

chi2 distributions

Figure 20:chi2 distribution using "standard" MC shape

Figure 21:chi2 distribution using Pibero shape

2008.04.16 Sided residual: Data Driven MC vs raw MC vs 2006 data

Ilya Selyuzhenkov April 16, 2008

Figure 1: Sided residual for raw MC (partonic pt 9-11)

Figure 2: Sided residual for data-driven MC (partonic pt 9-11)

Figure 3: Sided residual for data (pp Longitudinal 2006)

Different analysis cuts vs number of events which passed the cut

N_events : total number of di-jet events found by the jet-finder for gamma in eta region [1,2]
(Geant record is used to get this number)
cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
R_{3x3cluster} > 0.9 : Energy in 3x3 cluster of EEMC tower to the total jet energy.
R_EM^jet < 0.9 : neutral energy fraction cut for on away side jet
N_ch=0 : no charge tracks associated with a gamma candidate
N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
N_(5-strip clusler)^u > 3 : minimum number of strips in EEMC SMD u-plane cluster around peak
N_(5-strip cluster)^v > 3 : minimum number of strips in EEMC SMD v-plane cluster around peak
gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluser

Figure 4: Number of events which passed various cuts (MC data, partonic pt 9-11)

2008.04.17 Sided residual: Data Driven MC vs raw MC (partonic pt=5-35) vs 2006 data

Ilya Selyuzhenkov April 17, 2008

MC data for different pt weigted according to Michael Betancourt web page:
weight = xSection[ptBin] / xSection[max] / nFiles

Figure 1: Sided residual for raw MC (partonic pt 5-35)
(same plot for partonic pt 9-11)

Figure 2: Sided residual for data-driven MC (partonic pt 5-35)
(same plot for partonic pt 9-11)

Figure 3: Sided residual for data (pp Longitudinal 2006)

Figure 4: Sided residual for data (pp Longitudinal 2006)

Figure 5: Sided residual for data (pp Longitudinal 2006)

Figure 6: pt(gamma) from geant record vs
pt(gamma) from energy in 3x3 tower cluster and position for uv-intersection wrt vertex
(same on a linear scale)

Figure 7: pt(gamma) from geant record vs
pt(jet) as found by the jet-finder

Figure 8: gamma pt distribution:
data-driven MC (red) vs gamma-jet candidates from pp2006 longitudinal run (black).
MC distribution normalized to data at maximum for each preshower condition

Different analysis cuts vs number of events which passed the cut

N_events : total number of di-jet events found by the jet-finder for gamma in eta region [1,2]
(Geant record is used to get this number)
cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
R_{3x3cluster} > 0.9 : Energy in 3x3 cluster of EEMC tower to the total jet energy.
R_EM^jet < 0.9 : neutral energy fraction cut for on away side jet
N_ch=0 : no charge tracks associated with a gamma candidate
N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
N_(5-strip clusler)^u > 3 : minimum number of strips in EEMC SMD u-plane cluster around peak
N_(5-strip cluster)^v > 3 : minimum number of strips in EEMC SMD v-plane cluster around peak
gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluser

Figure 9: Number of events which passed various cuts (MC data, partonic pt 5-35)
Red: cuts applied independent
Black: cuts applied sequential from left to right

2008.04.23 Gamma-jet candidates: pp2006 data vs data-driven MC (gamma-jet and bg:jet-jet)

Ilya Selyuzhenkov April 23, 2008

Sided residual: pp2006 data vs data-driven MC (gamma-jet and bg:jet-jet)

MC data for different partonic pt are weigted according to Michael Betancourt web page:
weight = xSection[ptBin] / xSection[max] / nFiles

Figure 1:Sided residual for data-driven gamma-jet MC events (partonic pt 5-35)

Figure 2:Sided residual for data-driven jet-jet MC events (partonic pt 3-55)

Figure 3:Sided residual for data (pp Longitudinal 2006)

Figure 4:pt(gamma) vs pt(jet) for data-driven gamma-jet MC events (partonic pt 5-35)

Figure 5:pt(gamma) vs pt(jet) for data-driven jet-jet MC events (partonic pt 3-55)

Figure 6:pt(gamma) vs pt(jet) for data (pp Longitudinal 2006)

05 May

May 2008 posts

2008.05.05 pt-distributions, sided residual (data vs dd-MC g-jet and bg di-jet)

Ilya Selyuzhenkov May 05, 2008

Data samples:

pp2006(long) - 2006 pp production longitudinal data after applying gamma-jet aisolation cuts
(jet-tree sample: 4.114pb^-1 from Jamie script, 3.164 pb^1 analyses).
gamma-jet - Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV
bg jets - Pythia di-jet sample (~4M events). Partonic pt range 3-65 GeV

Figure 1:pt distribution. MC data are scaled to the same luminosity as data
(Normalization factor: Luminosity * sigma / N_events).

Figure 2:Integrated gamma yield vs pt.
For each pt bin yield is defined as the integral from this pt up to the maximum available pt.
MC data are scaled to the same luminosity as data.

Figure 3:Signal to background ratio (all results divided by the data)

Sided residual: pp2006 data vs data-driven MC (gamma-jet and bg:jet-jet)

You can find sided residual 2-D plots here

Figure 4:Maximum sided residual for pt_gamma>7GeV; pt_jet>7GeV

Figure 5:Fitted peak for pt_gamma>7GeV; pt_jet>7GeV

Figure 6:Max data tail for pt_gamma>7GeV; pt_jet>7GeV

Figure 7:Max minus min data tails for pt_gamma>7GeV; pt_jet>7GeV

Figure 8:Shower shapes pt_gamma>7GeV; pt_jet>7GeV

2008.05.08 y:x EEMC position for gamma-jet candidates

Ilya Selyuzhenkov May 08, 2008

y:x EEMC position for gamma-jet candidates

Figure 1:y:x EEMC position for gamma-jet candidates:
Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.

Figure 2:y:x EEMC position for gamma-jet candidates:
Pythia QCD bg sample (~4M events). Partonic pt range 3-65 GeV.

Figure 3:y:x EEMC position for gamma-jet candidates:
pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger]

Figure 3b:y:x EEMC position for gamma-jet candidates:
pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger]
pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.

high u vs. v strip for gamma-jet candidates

Figure 4:High v-strip vs high u-strip.
Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.

Figure 5:High v-strip vs high u-strip:
Pythia QCD bg sample (~4M events). Partonic pt range 3-65 GeV.

Figure 6:High v-strip vs high u-strip:
pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger]

Figure 6b:High v-strip vs high u-strip:
pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger]
pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.

2008.05.09 Gamma-jet candidates pt-distributions and TPC tracking

Ilya Selyuzhenkov May 09, 2008

Detector eta cut study (1< eta < 1.4):

For a three data samples (pp2006 [long], MC gamma-jet, and MC QCD background events)
the EEMC detector eta cut of 1< eta < 1.4 has been applied.
Although a poor statistics available for MC background QCD sample,
the signal to background ratio (red to green line ratio)
getting closer to 1:3 (expected signal to background ratio from Les study).
Distributions withoud EEMC detector eta cut.
y:x position within EEMC detector for gamma-jet candidates.

Figure 1:Gamma pt distribution. MC data are scaled to the same luminosity as data
(Normalization factor: Luminosity * sigma / N_events).

Figure 2:Gamma yield vs pt. MC data are scaled to the same luminosity as data.

Figure 3:Signal to background ratio (MC results are normalized to the data)

2008.05.14 Gamma-cluster to jet energy ratio and away side jet pt matching

Ilya Selyuzhenkov May 14, 2008

Gamma-cluster to jet1 energy ratio

Correlation between gamma-candidate 3x3 cluster energy ratio (R_cluster) and
number of EEMC towers in a jet1 can be found here (Fig. 4).
Gamma pt distribution, yield and signal to background ratio plots
for a cut of R_cluster >0.9 can be found here (Figs. 1-3).
Gamma pt distribution, yield and signal to background ratio plots
for a cut of R_cluster >0.99 are shown below in Figs. 1-3.
One can see that by going from R_cluster>0.9 to R_cluster>0.99
improves signal to background ratio from ~ 1:10 to ~ 1:5 for gamma pt>10 GeV

Figure 1:Gamma pt distribution for R_cluster >0.99.
MC results scaled to the same luminosity as data
(Normalization factor: Luminosity * sigma / N_events).

Figure 2:Integrated gamma yield vs pt for R_cluster >0.99
For each pt bin yield is defined as the integral from this pt up to the maximum available pt.
MC results scaled to the same luminosity as data.

Figure 3:Signal to background ratio for R_cluster >0.99 (all results divided by the data)
Compare this figure with that for R_cluster>0.9 (Fig. 3 at this link)

Gamma and the away side jet pt matching

Figure 4: pt asymmetry between gamma and the away side jet (R_cluster >0.9)
for a three data samples (pp2006[long] data, gamma-jet MC, QCD jets background).
pt cut of 7 GeV for both gamma and jet has been applied.

Figure 5: signal to background ratio (R_cluster >0.9)
as a function of pt asymmetry between gamma and the away side jet
pt cut of 7 GeV for both gamma and jet has been applied.

Figure 6: pt asymmetry between gamma and the away side jet (R_cluster >0.99)
for a three data samples (pp2006[long] data, gamma-jet MC, QCD jets background).
pt cut of 7 GeV for both gamma and jet has been applied.

Figure 7: signal to background ratio
as a functio of pt asymmetry between gamma and the away side jet (R_cluster >0.99)
pt cut of 7 GeV for both gamma and jet has been applied.

Figure 8: pt asymmetry between gamma and the away side jet (R_cluster >0.99)
for a three data samples (pp2006[long] data, gamma-jet MC, QCD jets background).
pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.

Figure 9: signal to background ratio
as a function of pt asymmetry between gamma and the away side jet (R_cluster >0.99)
pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.

2008.05.15 Vertex z distribution for pp2006 data, MC gamma-jet and QCD jets events

Ilya Selyuzhenkov May 15, 2008

Figure 1:Vertex z distribution for pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger]
Note: In the upper right plot (pre1=0, pre2>0) one can see
a hole in the acceptance in the range bweeeen z_vertex -10 to 30 cm (probably due to SVT construction)

Figure 1b:Vertex z distribution for pp2006 (same as Fig. 1, but on a linear scale)

Figure 2:Vertex z distribution for three different data samples
MC results scaled to the same luminosity as data

Figure 3:Vertex z distribution for three different data samples
pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.

2008.05.20 Shower shapes sorted by pre-shower, z-vertex and gamma's eta, phi, pt

Ilya Selyuzhenkov May 20, 2008

Gamma-jet algorithm and isolation cuts:

Selecting only di-jet events identified by the STAR jet finder algorithm,
with jets pointing opposite in azimuth:
cos(phi_jet1 - phi_jet2) < -0.8
Select jet1 with a maximum neutral energy fraction (R_EM1).
This is our gamma candidate, for which we further require:
- No charge tracks associated with jet1 (default jet radius is 0.7):
  nChargeTracks_jet1 = 0
  Note, that this charge track veto only works
  in the EEMC region where we do have TPC tracking
- No barrel towers associated with jet1 (pure EEMC jet):
  nBarrelTowers_jet1 = 0
- Ratio of the energy in the 3x3 EEMC high tower cluster
  to the total jet energy to be:
  R_cluster>0.99 (previous, softer, cut was 0.9)
For the second jet2 (away side jet) we require:
- That jet2 has at least ~10% of hadronic energy:
  R_EM2<0.9
Additional gamma candidate QA requirements:
- Matching between EEMC SMD uv-strip cluster with a 3x3 cluster of EEMC towers.
  (in addition reject events for which we can not idetify uv-strip intersection)
- Minimum number of strips in 5-strip EEMC SMD uv-plane clusters to be greater that 3.

Data sample:

pp2006(long) - 2006 pp production longitudinal data after applying gamma-jet isolation cuts
(note the new R_cluster>0.99 cut)

Shower shapes sorted by pre-shower, z-vertex and gamma's eta, phi, pt

Note, that all shapes are normalized at peak to unity

Figure 1:Shower shapes for different detector eta bins

Figure 2:Shower shapes for different detector phi bins

Figure 3:Shower shapes for different gamma pt bins

Figure 4:Shower shapes for different z-vertex bins

2008.05.21 EEMC SMD data-driven library: some eta-meson QA plots

Ilya Selyuzhenkov May 21, 2008

EEMC SMD data-driven library: some eta-meson QA plots

Data sample:

Subset of 441 eta-meson candidates from Will's analysis.
additional QA info (detector eta, pre1, pre2, etc)
has been added to pi0-tree reader script:
/star/institutions/iucf/wwjacobs/newEtas_fromPi0finder/ReadEtaTree.C
pi0 trees from this RCF directory has been used to regenerate etas NTuple:
/star/institutions/iucf/wwjacobs/newEtas_fromPi0finder/out_23/

Some observations:

eta-meson purity within the invariant mass region [0.5, 0.65] is about 72%
Most of the eta-candidates has detector pseudorapidity less or about 1.4,
what may limits applicability of data-driven shower shapes
derived from these candidates for higher pseudo-rapidity region,
where we have most of the background for the gamma-jet
analysis due to lack of TPC tracking
z-vertex distribution is very asymmetric, and peaked around -50cm.
Only a few candidates has a positive z-vertex values.

Figure 1: Eta-meson invariant mass with signal and background fits and ratio (upper left).
Pseudorapidity [detector and wrt vertex] distributions (right top and bottom plots),
vertex z distributions (bottom left)

Figure 2:2D plots for the eta-meson invariant mass vs
azimuthal angle (upper left), pseudorapidity (upper right),
z-vertex (bottom right), and detector pseudorapidity (bottom right)

2008.05.27 Shower shapes: pp2006 data, MC gamma-jet and QCD jets, gammas from eta

Ilya Selyuzhenkov May 27, 2008

Shower shapes and triple Gaussian fits for gammas from eta-meson

Figure 1: Shower shapes and triple Gaussian fits for photons from eta-meson
sorted by different conditions of EEMC 1st and 2nd pre-shower layers.
Note: All shapes have been normalized at peak to unity

Triple Gaussian fit parameters:
Pre1=0 Pre2=0
0.669864*exp(-0.5*sq((x-0.46016)/0.574864))+0.272997*exp(-0.5*sq((x-0.46016)/-1.84608))+0.0585682*exp(-0.5*sq((x-0.46016)/5.49802))
Pre1=0 Pre2>0
0.0694729*exp(-0.5*sq((x-0.493468)/5.65413))+0.615724*exp(-0.5*sq((x-0.493468)/0.590723))+0.314777*exp(-0.5*sq((x-0.493468)/2.00192))
Pre1>0 Pre2>0
0.0955638*exp(-0.5*sq((x-0.481197)/5.59675))+0.558661*exp(-0.5*sq((x-0.481197)/0.567596))+0.345896*exp(-0.5*sq((x-0.481197)/1.9914))

Shower shapes: pp2006, MC gamma-jet and QCD jets, gammas from eta

Shower shapes comparison between different data sets:

gammas from eta-meson decay. Obtained from Will's eta-meson analysis
pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Some observations:

Shapes for gammas from eta-meson decay
are in a good agreement with those from MC gamma-jet sample
(compare red squares with blue triangle in Fig. 2 and 3).

MC gamma-jet shapes obtained by running a full gamma-jet reconstruction algorithm,
and this agreement indicates that we are able to reconstruct gamma shapes
which we put in with data-driven shower shape library.
MC gamma-jet shapes match pp2006 data shapes
for pre1=0 condition, where we expect to be very efficient in background rejection
(compare red squares with black circles in upper plots of Fig. 2 and 3).

This indicates that we are able to reproduce EEMC SMD of direct photons with data-driven Monte-Carlo.
There is no match between Monte-Carlo QCD background jets and pp2006 data
for the case when both pre-shower layer fired (pre1>0 and pre2>0).
(compare green triangles with black circes in bottom right plots of Fig.2 and 3).
This is the region where we know background dominates our gamma-jet candidates.

This shows that we still do not reproduce SMD response for our background events
in our data-driven Monte-Carlo simulations
(note, that in Monte-Carlo we replace SMD response with real shapes for all background photons
the same way we do it for direct gammas).

Figure 2: Shower shapes comparison between different data sets.
Shapes for gamma-jet candidates obtained with the same gamma-jet reconstruction algorithm
for three different data samples (pp2006, gamma-jet and QCD jets MC).
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

Figure 3:Same as Fig. 2, but with no cuts on gamma and jet pt.
All shapes are similar to those in Fig. 2 with an additional pt cuts.
Note, that blue triangles are the same as in Fig. 2.

2008.05.30 Eta, phi, and pt distributions for gamma and jet from MC and pp2006 data

Ilya Selyuzhenkov May 30, 2008

Three data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Figure 1: Gamma eta distribution.
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

Figure 2: Gamma pt distribution.
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

Figure 3: Gamma phi distribution.
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

Figure 4: Away side jet eta distribution.
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

Figure 5: Away side jet pt distribution.
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

Figure 6: Gamma-jet delta pt distribution.
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

Figure 7: Gamma-jet delta eta distribution.
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

Figure 8: Gamma-jet delta phi distribution.
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

06 Jun

June 2008 posts

2008.06.04 Gamma cluster energy in various EEMC layers: data vs MC

Ilya Selyuzhenkov June 04, 2008

Gamma cluster energy in various EEMC layers: data vs MC

Three data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Figure 1: Gamma candidate EEMC pre-shower 1 energy (3x3 cluster).
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

Figure 2: Gamma candidate EEMC pre-shower 2 energy (3x3 cluster).
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

Figure 3: Gamma candidate EEMC tower energy (3x3 cluster).
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

Figure 4: Gamma candidate EEMC post-shower energy (3x3 cluster).
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

Figure 5: Gamma candidate EEMC SMD u-plane energy [5-strip cluster] (Figure for v-plane)
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

Difference between total and gamma candidate cluster energy for various EEMC layers

Figure 6: Total minus gamma candidate (3x3 cluster) energy in EEMC pre-shower 1 layer
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

Figure 7: Total minus gamma candidate (3x3 cluster) energy in EEMC pre-shower 2 layer
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

Figure 8: Total minus gamma candidate (3x3 cluster) energy in EEMC tower
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

Figure 9: Total minus gamma candidate (3x3 cluster) energy in EEMC post-shower layer
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

Figure 10: Total (sector) energy minus gamma candidate (5-strip cluster) energy in EEMC SMD[u-v] layer
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.

2008.06.09 STAR White paper plots (pt distribution: R_cluster 0.99 and 0.9 cuts)

Ilya Selyuzhenkov June 09, 2008

Gamma pt distribution: data vs MC (R_cluster 0.99 and 0.9 cuts)

Three data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Numerical values for different pt-bins from Fig. 1-2

Figure 1: Gamma pt distribution for R_cluster >0.9.
No energy in both pre-shower layer (left plot), and
No energy in pre-shower1 and non-zero energy in pre-shower2 (right plot)
Same figure for R_cluster>0.99 can be found here

Figure 2: Gamma pt distribution for R_cluster >0.9.
No energy in first EEMC pre-shower1 layer (left plot), and
non-zero energy in pre-shower1 (right plot)
For more details (yield, ratios, all pre12 four conditions, etc) see figures 1-3 here.

Figure 3: Gamma pt distribution for R_cluster >0.99.
For more details (yield, ratios, all pre12 four conditions, etc) see figures 1-3 here.

2008.06.10 Gamma-jet candidate longitudinal double spin asymmetry

Ilya Selyuzhenkov June 10, 2008

Note: No background subtraction has been done yet

The case of pre-shower1=0 (left plots) roughly has 1:1 signal to background ratio,
while pre-shower1>0 (right plots) have 1:10 ratio (See MC to data comparison for details).

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts,
plus two additional vertex QA cuts:
a) |z_vertex| < 100 and
b) 180 < bbcTimeBin < 300
Polarization fill by fill: blue and yellow
Relative luminosity by polarization fills and runs: relLumi06_070614.txt.gz
Equations used to calculate A_LL from the data: pdf file

Figure 1: Gamma-jet candidate A_LL vs gamma pt.
Figures for related epsilon_LL and 1/Lum scaled by a factor 10^7
(see pdf/html links above for epsilon_LL and 1/Lum definitions)

Figure 2: Gamma-jet candidate A_LL vs x_gluon.
Figures for related epsilon_LL and 1/Lum scaled by a factor 10^7

Figure 3: Gamma-jet candidate A_LL vs x_quark.
Figures for related epsilon_LL and 1/Lum scaled by a factor 10^7

Figure 4: Gamma-jet candidate A_LL vs away side jet pt.
Figures for related epsilon_LL and 1/Lum scaled by a factor 10^7

2008.06.18 Photon-jet reconstruction with the EEMC detector (talk at the STAR Collaboration meeting)

Ilya Selyuzhenkov June 18, 2008

Slides

Photon-jet reconstruction with the EEMC detector - Part 1: pdf or odp

Talk outline (preliminary)

Introduction and motivation
Data samples (pp2006, MC gJet, MC QCD bg)
and gamma-jet reconstruction algorithm:
- di-jets, isolation cuts, EEMC 3x3 cluster
- Data sets used and sample EEMC response to a photon candidate
Comparing pp2006 with Monte-Carlo simulations scaled to the same luminosity
(EEMC pre-shower sorting):
EEMC SMD shower shapes from different data samples
(pp2006 and data-driven Monte-Carlo):
- Shower shapes sorted by energy in pre-shower 1 and 2 layers
- pp2006 shower shapes sorted by vertex z, detector eta, phi, pt
Sided residual plots: pp2006 vs data-driven Monte-Carlo
(gammas from eta meson: 3 gaussian fits)
- Sided residual: 2D correlations
- Sided residual: 1D projections
Various cuts study:
- R_cluster cut of 0.99 and 0.9 comparison
  (Cluster energy distribution vs number of EEMC towers fired: Fig.4)
- Charge particle veto and TPC tracking
- Away side jet pt matching
Some QA plots:
- Gamma candidate u- vs. v- plane high strip distribution; x:y position
- Vertex z distribution
A_LL reconstruction technique:
- Method and gamma-jet candidates raw asymmetry
Work in progress... To do list:
- Understading MC background and pp2006 data shower shapes discrepancy
- Implementing sided residual technique with shapes sorted by pre1&2 (eta, sector, etc?)
- Tuning analysis cuts
- Quantifying signal to background ratio
- Background subtraction for A_LL, ...
- What else?
Talk summary

07 Jul

July 2008 posts

2008.07.07 Pre-shower1 < 5MeV cut study

Ilya Selyuzhenkov July 07, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Figure 1: Correlation between 3x3 cluster energy in pre-shower2 vs. pre-shower1 layers

Figure 1a: Distribution of the 3x3 cluster energy in pre-shower1 layer (zoom in for Epre1<0.03 region)
(pp2006 data vs. MC gamma-jet and QCD events)

Figure 2: Shower shapes after pre-shower1 < 5MeV cut.
Shapes are narrower than those without pre1 cut (see Fig. 2)

Figure 3: Gamma pt distribution with pre-shower1 < 5MeV cut.
Compare with distribution withoud pre-shower1 (Fig. 3)

Sided residual (before and after pre-shower1 < 5MeV cut)

Figure 4: Fitted peak vs. maximum sided residual (no pre-shower1 cuts)
Only points for pp2006 data are shown.

Figure 5: Fitted peak vs. maximum sided residual (after pre-shower1 < 5MeV cut).
Only points for pp2006 data are shown.
Note that distribution for pre1>0,pre2>0 case are narrower
compared to that in Fig.4 (without pre-shower1 cuts).

Figure 6: Distribution of maximum sided residual with pre-shower1 < 5MeV cut.

2008.07.16 Gamma-gamma invariant mass cut study

Ilya Selyuzhenkov July 16, 2008

Three data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

My simple gamma-gamma finder is trying to
find a second peaks (clusters) in each SMD u and v planes,
match u and v plane high strip intersections,
and calculate the invaraint mass from associated tower energies (3x3 cluster)
according to the energy sharing between SMD clusters.

Figure 1: Gamma-gamma invariant mass plot.
Only pp2006 data are shown: black: no pt cuts, red: gamma pt>7GeV and jet pt>5 GeV.
Clear pi0 peak in the [0.1,0.2] invariant mass region.
Same data on the log scale

Gamma pt distributions

Figure 2: Gamma pt distribution (no inv mass cuts).

Figure 3: Gamma pt distribution (m_invMass<0.11 or no second peak found).
This cut improves signal to background ratio.

Figure 4: Gamma pt distribution (m_invMass>0.11).
Mostly background events.

Shower shapes

Figure 5: Shower shapes (no pre1 and no invMass cuts).
Good match between shapes in case of no energy in pre-shower1 layer (pre1=0 case).

Figure 6: Shower shapes (pre1<5MeV, no invMass cuts).
For pre1&2>0 case shapes getting closer to ech other, but still do not match.

Figure 7: Shower shapes (cuts: pre1<5MeV, invMass<0.11 or no second peak found).
Note, the surprising agreement between eta-meson shapes (blue) and data (black).

Gamma-gamma invariant mass plots

Figure 8: Invariant mass distribution (MC vs. pp2006 data): no pre1 cut

Figure 9: Invariant mass distribution (MC vs. pp2006 data): pre1<5MeV
Left side is the same as in Figure 8

Figure 10: Invariant mass distribution (MC vs. pp2006 data): pre1>5MeV
Left side plot is empty, since there is no events with [pre1=0 and pre1>5MeV]

2008.07.22 Photons from eta-meson: library QA

Ilya Selyuzhenkov July 22, 2008

Shower shapes

Figure 1: Shower shapes: no energy cuts, only 12 strips from peak (left u-plane, right v-plane).

Figure 1a: Shower shapes: no energy cuts, 150 strips from peak (left u-plane, right v-plane).

Figure 2: Shower shapes Energy>8GeV (left u-plane, right v-plane).

Figure 3: Shower shapes Energy<=8GeV (left u-plane, right v-plane).

One dimensional distributions

Figure 4: Tower energy.

Figure 5: Post-shower energy.

Figure 6: Pre-shower1 energy.

Figure 7: Pre-shower2 energy.

Figure 8: Number of library candidates per sector.

Correlation plots

Figure 9: Transverse momentum vs. energy.

Figure 10: Distance from center of the detector vs. energy.

Figure 11: x:y position.

Figure 12: u- vs. v-plane position.

2008.07.29 Shower shape comparison with new dd-library bins

Ilya Selyuzhenkov July 29, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Latest data-driven shower shape replacement library:

Four pre-shower bins: pre1,2=0, pre1=0,pre2>0 pre1<4MeV, pre1>=4MeV
plus two energy bins (E<8GeV, E>=8GeV)

Figure 1: Shower shapes for u-plane [12 strips]
Shower shapes for the library are for the E>8GeV bin.

Figure 2: Shower shapes for v-plane [12 strips]

Figure 3: Shower shapes for u-plane [expanded to 40 strips]

Figure 4: Shower shapes for v-plane [expanded to 40 strips]

08 Aug

August 2008 posts

2008.08.14 Shower shape with bug fixed dd-library

Ilya Selyuzhenkov August 14, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Data-driven maker with bug fixed multi-shape replacement:

Four pre-shower bins: pre1,2=0, pre1=0,pre2>0 pre1<4MeV, pre1>=4MeV
plus two energy bins (E<8GeV, E>=8GeV)

Figure 1: Shower shapes for u-plane [12 strips]
Shower shapes for the library are for the E>8GeV bin.
Open squares and triangles represents raw [without dd-maker]
MC gamma-jet and QCD background shower shapes respectively

Figure 2: Shower shapes for v-plane [12 strips]

Figure 3: Shower shapes for u-plane [expanded to 40 strips]
Dashed red and green lines represents raw [without dd-maker]
MC gamma-jet and QCD background shower shapes respectively

Figure 4: Shower shapes for v-plane [expanded to 40 strips]

2008.08.19 Shower shape from pp2008 vs pp2006 data

Ilya Selyuzhenkov August 19, 2008

Data sets:

pp2006 - STAR 2006 ppProductionLong data (~ 3.164 pb^1)
"eemc-http-mb-l2gamma" trigger after applying gamma-jet isolation cuts.
pp2008 - STAR ppProduction2008 (~ 5.9M events)
"fmsslow" trigger after applying gamma-jet isolation cuts.
[Only ~13 candidates has been selected before pt-cuts]
gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Note: Due to lack of statistics for 2008 data, no pt cuts applied on gamma-jet candidates for both 2006 and 2008 date.

Figure 1: Shower shapes for u-plane [pp2006 data: eemc-http-mb-l2gamma trigger]

Figure 2: Shower shapes for v-plane [pp2006 data: eemc-http-mb-l2gamma trigger]

Figure 3: Shower shapes for u-plane [pp2008 data: fmsslow trigger]

Figure 4: Shower shapes for v-plane [pp2008 data: fmsslow trigger]

2008.08.25 di-jets from pp2008 vs pp2006 data

Ilya Selyuzhenkov August 25, 2008

Data sets:

pp2006 - ppProductionLong [triggerId:137213] (day 136 only)
pp2008 - ppProduction2008 [triggerId:220520] (Jan's set of MuDst from day 047)

Event selection:

Run jet finder and select only di-jet events [adopt jet-finder script from Murad's analysis]
Define jet1 as the jet with largest neutral energy fraction (NEF), and jet2 - the jet with a smaller NEF
Require no EEMC towers associated with jet1
Select trigger (see above) and require vertex to be found

Figure 1: Vertex z distribution (left: pp2008; right: 2006 data)

Figure 2: eta vs. phi distribution for the jet1 (jet with largest NEF) .

Figure 3: eta vs. z-vertex distribution for the jet1 (jet with largest NEF) .

Figure 4: eta vs. z-vertex distribution for the second jet.

Figure 5: Transverse momentum distribution for jet1.

Figure 6: Number of barrel towers associated with jet1.

Figure 7: Number of charge tracks associated with jet1.

2008.08.26 Shower shape: more constrains for pre1>4E-3 bin

Ilya Selyuzhenkov August 26, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Data-driven library:

Four pre-shower bins: pre1,2=0, pre1=0,pre2>0 pre1<4MeV, pre1>=4MeV
plus two energy bins (E<8GeV, E>=8GeV)

Figure 1: Pre-shower1 energy distribution for Pre1>4 MeV:
Eta meson library for E>8GeV bin [left] and data vs. MC results [right].

Figure 2: Shower shapes for v-plane [Pre1<10MeV cut]

Figure 3: Shower shapes for u-plane [Pre1<10MeV cut]

Maximum side residual plots

Definitions for side residual plot (F_peak, F_tal, D_tail) can be found here
For a moment same 3-gaussian shape is used to fit SMD response for all pre-shower bins.
Algo needs to be updated with a new shapes sorted by pre-shower bins.

Figure 4: Sided residual plot for pp2006 data only [Pre1<10MeV cut]

Figure 5: Sided residual projection on "Fitted Peak" axis [Pre1<10MeV cut]

Figure 6: Sided residual projection on "tail difference" axis [Pre1<10MeV cut]

2008.08.27 Gamma-jet candidates detector position for different pre-shower conditions

Ilya Selyuzhenkov August 27, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Figure 1: High u vs. v strip id distribution for different pre-shower conditions.
Left column: QCD jets, middle column: gamma-jet, right columnt: pp2006 data

Figure 2: x vs. y position of the gamma-candidate within EEMC detector
for different pre-shower conditions.
Left column: QCD jets, middle column: gamma-jet, right columnt: pp2006 data

Figure 3:Reconstructed vs. generated (from geant record) gamma pt for the MC gamma-jet sample.
Pre-shower1<10MeV cut applied.

Figure 4:Reconstructed vs. generated (from geant record) gamma eta for the MC gamma-jet sample.
Pre-shower1<10MeV cut applied.

Figure 5:Reconstructed vs. generated (from geant record) gamma phi for the MC gamma-jet sample.
Pre-shower1<10MeV cut applied.

09 Sep

September 2008 posts

2008.09.02 Shower shape fits

Ilya Selyuzhenkov September 02, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Shower shape fitting procedure:

Fit with single Gaussian shape using 3 highest strips
Fit with double Gaussian using 5 strips from each side of the peak [11 strips total]
First Gaussian parameters are fixed from the step above
Re-fit with double Gaussian with initial parameters from step 2 above
Fit with triple Gaussian [fit range varies from 9 to 15 strips, default is 12 strips, see below]
Initial parameters for the first two Gaussian are fixed from step 3 above
Fit with triple Gaussian with initial parameters from step 4 above
(releasing all parameters except mean values)

Fitting function "[0]*(exp ( -0.5*((x-[1])/[2])**2 )+[3]*exp ( -0.5*((x-[4])/[5])**2 )+[6]*exp ( -0.5*((x-[7])/[8])**2 ))"

Fit results for MC gamma-jet data sample

Figure 1: MC gamma-jet shower shapes and fits for u-plane
Results from single, double and triple Gaussian fits (using from 9 to 15 strips) are shown.

Figure 2: Same as figure 1. but from v-plane

Figure 3: MC gamma-jet results using triple Gaussian fits within 12 strips from a peak.
Left: u-plane. Right: v-plane

Figure 4: Combined fit results from MC gamma-jet sample

Figure 5: Fitting parameters [see equation for the fit function above].
Note, that parameters 1, 4, and 7 (peak position) has the same value.

Numerical fit results:

pre1=0 pre2=0 [u]: 0.602039*((exp(-0.5*sq((x-0.491324)/0.605927))+(0.578161*exp(-0.5*sq((x-0.491324)/2.05454))))+(0.0937517*exp(-0.5*sq((x-0.491324)/6.37656))))
pre1=0 pre2=0 [v]: 0.729744*((exp(-0.5*sq((x-0.480945)/0.621631))+(0.327792*exp(-0.5*sq((x-0.480945)/2.01717))))+(0.0410935*exp(-0.5*sq((x-0.480945)/6.49599))))
pre1=0 pre2>0 [u]: 0.725212*((exp(-0.5*sq((x-0.474451)/0.560416))+(0.3332*exp(-0.5*sq((x-0.474451)/1.91957))))+(0.0611053*exp(-0.5*sq((x-0.474451)/5.34357))))
pre1=0 pre2>0 [v]: 0.686446*((exp(-0.5*sq((x-0.536662)/0.650485))+(0.388429*exp(-0.5*sq((x-0.536662)/1.99118))))+(0.0712328*exp(-0.5*sq((x-0.536662)/5.64637))))
0 <4MeV [u]: 0.612486*((exp(-0.5*sq((x-0.485717)/0.592415))+(0.55846*exp(-0.5*sq((x-0.485717)/1.87214))))+(0.0749598*exp(-0.5*sq((x-0.485717)/6.12462))))
0 <4MeV [v]: 0.651584*((exp(-0.5*sq((x-0.486876)/0.652023))+(0.450767*exp(-0.5*sq((x-0.486876)/2.07667))))+(0.0864232*exp(-0.5*sq((x-0.486876)/5.84357))))
4 <10MeV [u]: 0.621905*((exp(-0.5*sq((x-0.496841)/0.632917))+(0.512575*exp(-0.5*sq((x-0.496841)/1.97482))))+(0.0927374*exp(-0.5*sq((x-0.496841)/6.10844))))
4 <10MeV [v]: 0.634943*((exp(-0.5*sq((x-0.505378)/0.660763))+(0.480929*exp(-0.5*sq((x-0.505378)/2.17312))))+(0.0788037*exp(-0.5*sq((x-0.505378)/6.21667))))

Fit results for pp2006 gamma-jet candidates

Figure 6: Same as Fig. 3, but for gamma-jet candidates from pp2006 data

Figure 7: Same as Fig. 5, but for gamma-jet candidates from pp2006 data

2008.09.09 Maximum sided residual with shower shapes sorted by uv- and pre-shower bins

Ilya Selyuzhenkov September 09, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Procedure to calculate maximum sided residual:

For each event fit SMD u and v energy distributions with
triple Gaussian functions from shower shapes analysis:

[0]*(exp(-0.5*((x-[1])/[2])**2)+[3]*exp(-0.5*((x-[1])/[4])**2)+[6]*exp(-0.5*((x-[1])/[5])**2))
Fit parameters sorted by various pre-shower conditions and u and v-planes can be found here
There are only two free parameters in a final fit: overall amplitude [0] and mean value [1]
Fit range is +-2 strips from the high strip (5 strips total).
Integrate energy from a fit within +-2 strips from high strip.
This is our peak energy from fit, F_peak.
Calculate tail energies on left and right sides from the peak for both data, D_tail, and fit, F_tail.
Tails are integrated up to 30 strips excluding 5 highest strips.
Determine maximum difference between D_tail and F_tail:
max(D_tail-F_tail). This is our maximum sided residual.
Plot F_peak vs. max(D_tail-F_tail). This is sided residual plot.
(implementation for this item is in progress)
Based on MC gamma-jet sided residual plot find a line (some polynomial function)
which will serve as a cut to separate signal and background.
Use that cut line to calculate signal to background ratio
and apply it for the real data analysis.

Figure 1: Maximum sided residual plots for different data sets and various pre-shower condition.
Columns [data sets]: 1. MC QCD background; 2. gamma-jet; 3. pp2006 data
Rows [pre-shower bins]: 1. pre1=0 pre2=0; 2. pre1=0, pre2>0; 3. 0<pre1<4MeV; 4. 4<pre1<10MeV
Results from u and v plane are combined as [U+V]/2

Figure 2: max(D_tail-F_tail) distribution (projection on horizontal axis from Fig.1)
Some observations:
Results for pp2006 and MC gamma-jet are consistent for pre1=0 pre2=0 case (upper left plot)
Results for pp2006 and MC QCD background jets are also in agrees for pre1>0 case (lower left and right plots)

Figure 3: F_peak distribution (projection on vertical axis from Fig.1)

2008.09.16 QA plots for maximum sided residual (obsolete)

Ilya Selyuzhenkov September 16, 2008

These results are obsolete.
Please use this link instead

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Notations used in the plots:

Fit peak energy:
F_peak - integral within +-2 strips from maximum strip
Maximum strip determined by fitting procedure.
Float value converted ("cutted") to integer value.
Data peak energy:
D_peak - energy sum within +-2 strips from maximum strip (the same strip Id as for F_peak).
Data tails:
D_tail^left and D_tail^right.
Energy sum from 3rd strip up to 30 strips on the
left and right sides from maximum strip (excludes strips which contributes to D_peak)
Fit tails:
F_tail^left and F_tail^right.
Same definition as for D_tail, but integrals are calculated from a fit function.
Maximum sided residual:
max(D_tail-F_tail)
Maximum of the data minus fit energy on the left and right sides from the peak.

Figure 1: D_peak from [U+V]/2.

Figure 2: U/V asymmetry for D_peak: [U-V]/[U+V]

Figure 3: F_peak from [U+V]/2.

Figure 4: U/V asymmetry for F_peak: [U-V]/[U+V]

Figure 5: (D_peak - F_peak)/D_peak asymmetry

Figure 6: Maximum sided residual from V vs. U plane.

Figure 7: (D_tail-F_tail)^right vs. (D_tail-F_tail)^left

2008.09.23 QA plots for maximum sided residual (bug fixed update)

Ilya Selyuzhenkov September 23, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
after applying gamma-jet isolation cuts (note: R_cluster > 0.9 is used below).
gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Notations used in the plots:

Fit peak energy:
F_peak - integral within +-2 strips from maximum strip
Maximum strip determined by fitting procedure.
Float value converted ("cutted") to integer value.
Data peak energy:
D_peak - energy sum within +-2 strips from maximum strip (the same strip Id as for F_peak).
Data tails:
D_tail^left and D_tail^right.
Energy sum from 3rd strip up to 30 strips on the
left and right sides from maximum strip (excludes strips which contributes to D_peak)
Fit tails:
F_tail^left and F_tail^right.
Same definition as for D_tail, but integrals are calculated from a fit function.
Maximum sided residual:
max(D_tail-F_tail)
Maximum of the data minus fit energy on the left and right sides from the peak.

Figure 1: D_peak from [U+V]/2.

Figure 2: (D_peak - F_peak)/D_peak asymmetry

Figure 3: Maximum sided residual from V vs. U plane.

Figure 4: (D_tail-F_tail)^right. (D_tail-F_tail)^left

2008.09.23 Right-left SMD tail asymmetries

Ilya Selyuzhenkov September 23, 2008

Figure 1: D_peak vs. [right-left] D_tail

Figure 2: [right-left]/[right-+left] D_tail

2008.09.23 Sided residual plot projection: toward s/b efficency/rejection plot

Ilya Selyuzhenkov September 23, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
after applying gamma-jet isolation cuts (note: R_cluster > 0.9 is used below).
gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Notations used in the plots:

Fit peak energy:
F_peak - integral within +-2 strips from maximum strip
Maximum strip determined by fitting procedure.
Float value converted ("cutted") to integer value.
Data peak energy:
D_peak - energy sum within +-2 strips from maximum strip (the same strip Id as for F_peak).
Data tails:
D_tail^left and D_tail^right.
Energy sum from 3rd strip up to 30 strips on the
left and right sides from maximum strip (excludes strips which contributes to D_peak)
Fit tails:
F_tail^left and F_tail^right.
Same definition as for D_tail, but integrals are calculated from a fit function.
Maximum sided residual:
max(D_tail-F_tail)
Maximum of the data minus fit energy on the left and right sides from the peak.

Maximum sided residual: MC vs. data comparison

Figure 1: Maximum sided residual plot
Top get more statistics for MC QCD sample plot is redone with a softer R_cluster > 0.9 cut

Figure 2: D_peak (projection on vertical axis for Fig. 1)
Upper left plot (no pre-shower fired case) reveals some difference
between MC gamma-jet and pp2006 data at lower D_peak values.
This difference could be due to background contribution at low energies.
Still needs more statistics for MC QCD jet sample to confirm that statement.

Figure 3: max(D_tail-F_tail) (projection on horisontal axis for Fig. 1)
One can get an idea of signal/background separation (red vs. black) depending on pre-shower condition.

Figure 4: Mean < max(D_tail-F_tail) > vs. D_peak (profile on vertical axis from Fig. 1)
For gamma-jet sample average sided residual is independent on D_peak energy
and has a slight positive shift for all pre-shower>0 conditions.
For large D_peak values (D_peak>0.16) MC gamma-jet and pp2006 data results are getting close to each other.
This corresponds to higher energy gammas, where we have a better signal/background ratio,
and thus more real gammas among gamma-jet candidates from pp2006 data.
(Note: legend's color coding is wrong, colors scheme is the same as in Fig. 3)

Figure 5: Mean < D_peak > vs. max(D_tail-F_tail) (profile on horisontal axis from Fig. 1)
For "no-preshower fired" case MC gamma-jet sample has a large average values than that from pp2006 data.
This reflects the same difference between pp2006 and MC gamma-jet sample at small D_peak values (see Fig. 2, upper left plot).
(Note: legend's color coding is wrong, colors scheme is the same as in Fig. 3)

Figure 6: D_peak vs. gamma pt

Figure 7: D_peak vs. gamma 3x3 tower cluster energy

Figure 8: 3x3 cluster tower energy distribution

Figure 9: Gamma pt distribution

Signal/background separation

The simplest way to get signal/background separation is to draw a straight line
on sided residual plot (Fig. 1) in such a way that
it will contains most of the counts (signal) on the left side,
and use a distance to that line for both MC and pp2006 data samples
as a discriminant for signal/background separation.
To get the distance to the straight line one can rotate sided residual plot
by the angle which corresponds to the slope of this line,
and then project it on "rotated" max(D_tail-F_tail) axis.

Figure 10: Shows "rotated" sided residual plot by "5/6*(pi/2)" angle (this angle has been picked by eye).
One can see that now most of the counts for gamma-jet sample (middle column)
are on the left side from vertical axis.

Figure 11: "Rotated" max(D_tail-F_tail) [projection on horizontal axis for Fig. 10]
Cut on "Rotated" max(D_tail-F_tail) can be used for signal/background separation.
From figure below one can see much better signal/background separation than in Fig. 3

Figure 12: "Rotated" D_peak [projection on vertical axis for Fig. 10]

Optimizing the shape of s/bg separation line

Ideally, instead of straight line one needs to use
an actual shape of side residual distribution for MC gamma-jet sample.
This shape can be extracted and parametrized by the following procedure:

Get slices from sided residual plot for different D_peak values
From each slice get max(D_tail-F_tail) value
for which most of the counts appears on its left side (for example 80%),
Fit these set of points {D_peak slice, max(D_tail-F_tail)} with a polynomial function

The distance to that polynomial function can be used to determine our signal/background rejection efficiency.

This work is in progress...
Just last one figure showing shapes for 6 slices from sided plot.

Figure 13: max(D_tail-F_tail) for different slices in D_peak (scaled by the integral for each slice)

2008.09.30 Sided residual: purity, efficiency, and background rejection

Ilya Selyuzhenkov September 30, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
after applying gamma-jet isolation cuts (note: R_cluster > 0.9 is used below).
gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Notations used in the plots:

Fit peak energy:
F_peak - integral within +-2 strips from maximum strip
Maximum strip determined by fitting procedure.
Float value converted ("cutted") to integer value.
Data peak energy:
D_peak - energy sum within +-2 strips from maximum strip (the same strip Id as for F_peak).
Data tails:
D_tail^left and D_tail^right.
Energy sum from 3rd strip up to 30 strips on the
left and right sides from maximum strip (excludes strips which contributes to D_peak)
Fit tails:
F_tail^left and F_tail^right.
Same definition as for D_tail, but integrals are calculated from a fit function.
Maximum sided residual:
max(D_tail-F_tail)
Maximum of the data minus fit energy on the left and right sides from the peak.

Determining cut line based on sided residual plot

Figure 1: Sided residual plot: D_peak vs. max(D_tail-F_tail)
Red lines show 4th order polynomial functions, a*x^4,
which have 80% of MC gamma-jet counts on the left side.
These lines are obtained independently for each of pre-shower condition
based on fit procedure shown in Fig. 3 below.

Figure 2: max(D_tail-F_tail) distribution
(projection on horizontal axis from sided residual plot, see Fig. 1 above)

Figure 3: max(D_tail-F_tail) [at 80%] vs. D_peak.
For each slice (bin) in D_peak variable, the max(D_tail-F_tail) value
which has 80% of gamma-jet candidates on the left side are plotted.

Lines represent fits to MC gamma-jet points (shown in red) using different fit functions
(linear, 2nd, 4th order polynomials: see legend for color coding).
Note, that in this plot D_peak values are shown on horizontal axis.
Consequently, to get 2nd order polynomial fit on sided residual plot (Fig. 1),
one needs to use sqrt(D_peak) function.
The same apply to 4th order polynomial function.

Figure 4: D_peak vs. horisontal distance from 4th order polinomial function to max(D_tail-F_tail) values.
(compare with Fig. 1: Now 80% of MC gamma-jet counts are on the left side from vertical axis)

Figure 5: Horizontal distance from 4th order polynomial function to max(D_tail-F_tail)
[Projection on horizontal axis from Fig. 4]
Based on this plot one can obtain purity, efficiency, and rejection plots (see Fig. 6 below)

Gamma-jet purity, efficiency, and QCD background rejection

Horizontal distance plotted in Fig. 5 can be used as a cut
separating gamma-jet signal and QCD-jets background,
and for each value of this distance one can define
gamma-jet purity, efficiency, and QCD-background rejection:

gamma-jet purity is defined as the ratio of
the integral on the left for MC gamma-jet data sample, N[g-jet]_left,
to the sum of the integrals on the left for MC gamma-jet and QCD jets, N[QCD]_left, data samples:
Purity[gamma-jet] = N[g-jet]_left/(N[g-jet]_left+N[QCD]_left)
gamma-jet efficiency is defined as the ratio of
the integral on the left side for MC gamma-jet data sample, N[g-jet]_left,
to the total integral for MC gamma-jet data sample, N[g-jet]:
Efficiency[gamma-jet] = N[g-jet]_left/N[g-jet]
QCD background rejection is defined as the ratio of
the integral on the right side for MC QCD jets data sample, N[QCD]_right,
to the total integral for MC QCD jets data sample, N[QCD]:
Rejection[QCD] = N[QCD]_right/N[QCD]

Figure 6: Shows:
purity[g-jet] vs. efficiency[g-jet] (upper left);
rejection[QCD] vs. efficiency[g-jet] (upper right);
purity[g-jet] vs. rejection[QCD] (lower left);
pp2006 to MC ratio, N[pp2006]/(N[g-jet]+N[QCD]), vs. horizontal distance from Fig. 5 (lower right)

10 Oct

October 2008 posts

2008.10.13 Jet trees for Michael's gamma filtered events

Ilya Selyuzhenkov October 13, 2008

I have finished production of jet trees for Michael's gamma filtered events

You can find jet and skim file lists in my directory at IUCF disk (RCF):

Jet trees: /star/institutions/iucf/IlyaSelyuzhenkov/simu/JetTrees/JetTrees.list
Skim trees: /star/institutions/iucf/IlyaSelyuzhenkov/simu/JetTrees/SkimTrees.list
Log files: /star/institutions/iucf/IlyaSelyuzhenkov/simu/JetTrees/LogFiles.list

Number of jet events is 1284581 (1020 files).
Production size, including archived log files, is 4.0G.

The script to run jet finder:

/star/institutions/iucf/IlyaSelyuzhenkov/simu/JetTrees/20081008_gJet/StRoot/macros/RunJetSimuSkimFinder.C

JetFinder and JetMaker code:

/star/institutions/iucf/IlyaSelyuzhenkov/simu/JetTrees/20081008_gJet/StRoot/StJetFinder
/star/institutions/iucf/IlyaSelyuzhenkov/simu/JetTrees/20081008_gJet/StRoot/StJetMaker

For more details see these threads of discussions:

jetfinding list: thread 814
photon reconstruction list: thread 271 and thread 256

2008.10.14 Purity, efficiency, and background rejection: R_cluster > 0.98

Ilya Selyuzhenkov October 14, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
after applying gamma-jet isolation cuts (note: R_cluster > 0.98 is used below).
gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Figure 1: Horizontal distance from 4th order polynomial function to max(D_tail-F_tail)
See this page for definition and more details

Figure 2:
purity[g-jet] vs. efficiency[g-jet] (upper left);
rejection[QCD] vs. efficiency[g-jet] (upper right);
purity[g-jet] vs. rejection[QCD] (lower left);
pp2006 to MC ratio, N[pp2006]/(N[g-jet]+N[QCD]), vs. horizontal distance (lower right)

2008.10.15 Comparison of gamma-jets from Michael's filtered events vs. old MC samples

Ilya Selyuzhenkov October 15, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
after applying gamma-jet isolation cuts (note: R_cluster > 0.9 is used below).
gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.
QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.
gamma-jet [old] - data-driven Pythia gamma-jet sample (~170K events).
Partonic pt range 5-35 GeV.
Details on jet trees production for Michael's gamma filtered events can be found here.
QCD jets [old] - data-driven Pythia QCD jets sample (~4M events).
Partonic pt range 3-65 GeV.

Some observations:

Both Fig. 1a vs. Fig. 1b shows good statistics for old and new (gamma-filtered) MC gamma-jet samples
Fig. 1c shows poor statistics for QCD background sample
within partonic pt range 5-10GeV (only 3 counts for "pre1=0 & pre2=0" condition).
Fig. 1d (new QCD sample) has much more counts in the same region,
but it is still only 20-25 entries for the case when
none of EEMC pre-shower layers fired (upper left corner - our purest gamma-jet sample).
This may be still insufficient for a various cuts systematic study.
Fig. 2 and Fig. 3 shows nice agreement between data and MC
for both old and new (gamma-filtered) MC samples.
For pre-shower1>0 case this agreement persists across full range of gamma's pt (7GeV and above).
Upper plots in Fig. 3 shows some difference between data and Monte-Carlo,
what could be effect from l2gamma trigger,
which has not been yet applied for MC events.

Figure 1a: partonic pt for gamma-jet [old] events
after analysis cuts and partonic pt bin weighting
(Note:Arbitrary absolute scale)

Figure 1b: partonic pt for gamma-jet [gamma-filtered] events after analysis cuts.
Michael's StBetaWeightCalculator has been used to caclulate partonic pt weights

Figure 1c: partonic pt for QCD jets [old] events
after analysis cuts and partonic pt bin weighting
(Note:Arbitrary absolute scale)

Figure 1d: partonic pt for QCD jets [gamma-filtered] events after analysis cuts.
Michael's StBetaWeightCalculator has been used to caclulate partonic pt weights

Figure 2: reconstructed gamma pt: old MC vs. pp2006 data (scaled to the same luminosity)

Figure 3: reconstructed gamma pt: gamma-filtered MC vs. pp2006 data (scaled to the same luminosity)

2008.10.15 Purity vs. efficiency from gamma-filtered events: R_cluster > 0.9 vs. R_cluster > 0.98

Ilya Selyuzhenkov October 15, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
after applying gamma-jet isolation cuts (note: R_cluster > 0.9 or > 0.98 is used below).
gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.
QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.

Gamma-jet candidates from MC gamma filtered events: R_cluster > 0.9

Figure 1: Horizontal distance from sided residual plot: R_cluster > 0.9
(see Figs. 1-5 from this post for horizontal distance definition)

Figure 2: Purity/efficiency/rejection, and data to MC[gamma-jet+QCD] ratio plots: R_cluster > 0.9
(see text above Fig. 6 from this post for purity, efficiency, and background rejection definition)

Gamma-jet candidates from MC gamma filtered events: R_cluster > 0.98

Figure 3: Reconstructed gamma pt: R_cluster > 0.98

Figure 4: Horizontal distance from sided residual plot: R_cluster > 0.98

Figure 5: Purity/efficiency/rejection, and data to MC[gamma-jet+QCD] ratio plots: R_cluster > 0.98

2008.10.21 Shower shapes, 5/25 strips cluster energy, raw vs. data-driven MC

Ilya Selyuzhenkov October 21, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
after applying gamma-jet isolation cuts (R_cluster > 0.9 is used below).
gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.
QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.

Some comments:

Overall comment: effect of data-driven shower shape replacement procedure
on QCD background events is small, except probably pre1=0 pre2=0 case.
Fig. 1-3, upper left plots (pre1=0 pre2=0) show that
average energy per strip in data-driven gamma-jet MC (i.e. solid red square in Fig. 3)
is systematically higher than that for pp2006 data (black circles in Fig. 3).

Note, that there is an agreement between SMD shower shapes
for pp2006 data and data-driven gamma-jet simulations
if one scales them to the same peak value
(Compare red vs. black in upper left plot from Fig. 1 at this link)
Fig. 4, upper left plot (pre1=0 pre2=0):
Integrated SMD energy from 25 strips
in raw gamma-jet simulations (red line) match pp2006 data (black line)
in the region where signal to background ratio is high, E_smd(25-strips)>0.1GeV.
This indicates that raw MC does a good job in
reproducing total energy deposited by direct photon.
Fig. 5, upper left plot (pre1=0 pre2=0):
There is mismatch between distributions of energy in 25 strips cluster
from data-driven gamma-jet simulations and pp2006 data.
This probably reflects the way we scale our library shower shapes
in data-driven shower shape replacement procedure.
Currently, the scaling factor for the library shape is calculated based on the ratio
of direct photon energy from Geant record to the energy of the library photon.
Our library is build out of photons from eta-meson decay,
which has been reconstructed by running pi0 finder.
The purity of the library is about 70% (see Fig. 1 at this post for more details).

The improvement of scaling procedure could be to
preserve total SMD energy deposited within 25 strips from raw MC,
and use that energy to scale shower shapes from the library.
Fig. 6, upper left plot (pre1=0 pre2=0):
Mismatch between integrated 5-strip energy for raw MC and pp2006 in Fig. 6
corresponds to "known" difference in shower shapes from raw Monte-Carlo and real data.

Figure 1: SMD shower shapes: data, raw, and data-driven MC (40 strips).
Vertical axis shows average energy per strip (no overall shower shapes scaling)

Figure 2: Shower shapes: data, raw, and data-driven MC (12 strips)

Figure 3: Shower shapes: data, raw, and data-driven MC (5 strips)

Figure 4: 25 strips SMD cluster energy for raw Monte-Carlo
(Note: type in x-axis lables, should be "25 strip peak" instead of 5)

Figure 5: 25 strips SMD cluster energy for data-driven Monte-Carlo

Figure 6: 5 strips SMD peak energy for raw Monte-Carlo

Figure 7: 5 strips SMD peak energy for data-driven Monte-Carlo

Figure 8:Energy from the right tail (up to 30 strips) for raw Monte-Carlo

Figure 9:Energy from the right tail (up to 30 strips) for data-driven Monte-Carlo

2008.10.27 SMD-based shower shape scaling: 25 strips cluster energy, raw vs. data-driven MC

Ilya Selyuzhenkov October 27, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
after applying gamma-jet isolation cuts (R_cluster > 0.9 is used below).
gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.
QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.

Shower shapes scaling options in data-driven maker:

scale = E_smd^geant / E_smd^library (default)
E_smd^geant is SMD energy associated with given photon
integrated over +/- 12 strips from raw Monte-Carlo,
and E_smd^library is SMD energy from +/- 12 strips for the library photon.
scale = E_Geant / E_library (used before in all posts)
E_Geant is thrown photon energy from Geant record,
and E_library is stand for energy of the library photon.

In all figures below (exept for pp2006 data and raw Monte-Carlo)
the SMD based shower shape scaling has been used.

Figure 1: SMD shower shapes: data, raw, and data-driven MC (40 strips).
Vertical axis shows average energy per strip (no overall shower shapes scaling)

Figure 2: Shower shapes: data, raw, and data-driven MC (12 strips)

Figure 3: Shower shapes: data, raw, and data-driven MC (5 strips)

Figure 4: 25 strips SMD cluster energy for data-driven Monte-Carlo
(SMD based shower shape scaling)

Figure 5: 25 strips SMD cluster energy for raw Monte-Carlo
Note, the difference between results in Fig. 4 and 5. for MC gamma-jets (shown in red)
at low energy (Esmd < 0.04) for pre1=0 pre2=0 case.
This effect is due to the "Number of strips fired in 5-strips cluster > 3" cut.
In data-driven Monte-Carlo we may have shower shapes
with small number of strips fired (rejected in raw Monte-Carlo)
to be replaced by library shape with different (bigger) number of strips fired.
This mostly affects photons which starts to shower
later in the detector and only fires few strips (pre1=0 pre2=0 case)

2008.10.30 Various cuts study (pt, Esmd, 8 strips replaced)

Ilya Selyuzhenkov October 30, 2008

Below are links to drupal pages
with various SMD energy distributions and shower shapes
for the following set of cuts/conditions:

Case A: pt > 7 GeV, +/- 12 strips replaced
Case B: pt > 7 GeV, +/- 8 strips replaced
Case C: pt > 7 GeV, +/- 12 strips replaced, E_smd(25strips) > 0.1
Case D: pt > 8.5 GeV, +/- 12 strips replaced

2008.10.30 Distance to cut line from sided residual

Figure 1: Case A

Figure 2:Case B

Figure 3:Case C

Figure 4:Case D

2008.10.30 SMD shower shapes: data, raw, and data-driven MC (12 strips)

Figure 1: Case A

Figure 2:Case B

Figure 3:Case C

Figure 4:Case D

2008.10.30 SMD shower shapes: data, raw, and data-driven MC (30 strips)

Figure 1: Case A

Figure 2: Case B

Figure 3: Case C

Figure 4: Case D

2008.10.30 Sided residual

Figure 1: Case A

Figure 2:Case B

Figure 3:Case C

Figure 4:Case D

2008.10.30 Smd emergy for left tail (-3 to -30 strips)

Figure 1: Case A

Figure 2:Case B

Figure 3:Case C

Figure 4:Case D

2008.10.30 Smd emergy for right tail (3 to 30 strips)

Figure 1: Case A

Figure 2:Case B

Figure 3:Case C

Figure 4:Case D

2008.10.30 Smd energy for 25 central strips

Figure 1: Case A

Figure 2:Case B

Figure 3:Case C

Figure 4:Case D

2008.10.30 Smd energy for 5 central strips

Figure 1: Case A

Figure 2:Case B

Figure 3:Case C

Figure 4:Case D

11 Nov

November 2008 posts

2008.11.06 Gamma-jet reconstruction with the Endcap EMC (Analysis status update)

Ilya Selyuzhenkov November 06, 2008

Gamma-jet reconstruction with the Endcap EMC (Analysis status update for Spin PWG)

latest version (pdf)
Version 3: pdf or odp (for spin PWG)
Version 2: pdf or odp (after phana comments)
Version 1: pdf or odp (draft for phana meeting)

2008.11.11 Yields vs. analysis cuts

Ilya Selyuzhenkov November 11, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
after applying gamma-jet isolation cuts (R_cluster > 0.9 is used below).
gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.
QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.

Figure 1: Reconstructed gamma pt for di-jet events and
Geant cuts: pt_gamma[Geant] > 7GeV and 1.05 < eta_gamma[Geant] < 2.0
Total integral for the histogram is: N_total = 5284
(after weighting different partonic pt bins and scaled to 3.164pb^-1).
Compare with number from Jim Sowinski study for
Endcap East+West gamma-jet and pt>7 GeV: N_Jim = 5472
( Jim's numbers are scaled to 3.164pb^-1 : [2539+5936]*3.164/4.9)

Figure 2: Reconstructed jet pt for di-jet events and the same cuts as in Fig. 1

Yield vs. various analysis cuts

List of cuts (sorted by bin number in Figs. 2 and 3):

N_events : total number of di-jet events found by the jet-finder
cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
R_{3x3cluster} > 0.9 : Energy in 3x3 cluster of EEMC tower to the total jet energy
R_EM^jet < 0.9 : neutral energy fraction cut for on away side jet
N_ch=0 : no charge tracks associated with a gamma candidate
N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak
N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak
gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster

Figure 3: Number of accepted events vs. various analysis cuts
The starting number of events (shown in first bin of the plots) is
the number of di-jets with reconstructed gamma_pt>7 GeV and jet_pt>5 GeV
upper left: cuts applied independently
upper right: cuts applied sequentially
lower left: ratio of pp2006 data vs. MC sum of gamma-jet and QCD-jets events (cuts applied independently)
lower right:ratio of pp2006 data vs. MC sum of gamma-jet and QCD jets events (cuts applied sequentially)

Figure 4: Number of accepted events vs. various analysis cuts
Data from Fig. 3 (upper plots) scaled to the initial number of events from first bin:
left: cuts applied independently
right: cuts applied sequentially

2008.11.18 Cluster isolation cuts: 2x1 vs. 2x2 vs. 3x3

Ilya Selyuzhenkov November 18, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
after applying gamma-jet isolation cuts (no R_cluster cut applied for this study).
gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.
QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.

2x1, 2x2, and 3x3 clusters definition:

3x3 cluster: tower energy sum for 3x3 patch around highest tower
2x2 cluster: tower energy sum for 2x2 patch
which are closest to 3x3 tower patch centroid.
3x3 tower patch centroid is defined based
on tower energies weighted wrt tower centers:
centroid = sum{E_tow * r_tow} / sum{E_tow}.
Here r_tow=(x_tow, y_tow) denotes tower center.
2x1 cluster: tower energy sum for high tower plus second highest tower in 3x3 patch
r=0.7 energy is calculated based on towers
within a radius of 0.7 (in delta phi and eta) from high tower

Cuts applied

all gamma-jet candidate selection cuts except 3x3/r=0.7 energy isolation cut

Results for 2x1, 2x2, and 3x3 clusters

Energy fraction in NxN cluster in r=0.7 radius
2x1, 2x2, 3x3 patch to jet radius of 0.7 energy ratios
Yield vs. NxN cluster energy fraction in r=0.7
For a given cluster energy fraction yield is defined as an integral on the right
Efficiency vs. NxN cluster energy fraction in r=0.7
For a given cluster energy fraction
efficiency is defined as the yield (on the right)
normalized by the total integral (total yield)

Efficiency vs. NxN cluster energy fraction in r=0.7

Figure 1b: 2x1/0.7 ratio

Figure 2b: 2x2/0.7 ratio

Figure 3b: 3x3/0.7 ratio

Figure 4b: 3x3/0.7 ratio but only using towers which passed jet finder threshold

Energy fraction in NxN cluster within r=0.7 radius

Figure 1a: 2x1/0.7 ratio

Figure 2a: 2x2/0.7 ratio

Figure 3a: 3x3/0.7 ratio

Figure 4a: 3x3/0.7 ratio but only using towers which passed jet finder threshold

Yield vs. NxN cluster energy fraction in r=0.7

Figure 1c: 2x1/0.7 ratio

Figure 2c: 2x2/0.7 ratio

Figure 3c: 3x3/0.7 ratio

Figure 4c: 3x3/0.7 ratio but only using towers which passed jet finder threshold

2008.11.21 Energy fraction from 2x1 vs. 2x2 vs. 3x3 or 0.7 radius: rapidity dependence

Ilya Selyuzhenkov November 21, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
after applying gamma-jet isolation cuts (no R_cluster cut applied for this study).
gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.
QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.

2x1, 2x2, and 3x3 clusters definition:

3x3 cluster: tower energy sum for 3x3 patch around highest tower
2x2 cluster: tower energy sum for 2x2 patch
which are closest to 3x3 tower patch centroid.
3x3 tower patch centroid is defined based
on tower energies weighted wrt tower centers:
centroid = sum{E_tow * r_tow} / sum{E_tow}.
Here r_tow=(x_tow, y_tow) denotes tower center.
2x1 cluster: tower energy sum for high tower plus second highest tower in 3x3 patch
r=0.7 energy is calculated based on towers
within a radius of 0.7 (in delta phi and eta) from high tower

Cuts applied

all gamma-jet candidate selection cuts except 3x3/r=0.7 energy isolation cut

Results

There are two sets of figures in links below:

Number of counts for a given energy fraction
Yield above given energy fraction
[figures with right integral in the caption]

Yield is defined as the integral above given energy fraction
up to the maximum value of 1

Gamma candidate detector eta < 1.5
(eta region where we do have most of the TPC tracking):

Gamma candidate detector eta > 1.5:
(smaller tower size)

Some observation

For pre1>0 condition (contains most of events)
yield in Monte-Carlo for eta > 1.5 case
is about factor of two different than that from pp2006 data,
while for eta < 1.5 Monte-Carlo yield agrees with data within 10-15%.
This could be due to trigger effect?
For pre1=0 case yiled for both eta > 1.5 and eta < 1.5 are different in data and MC
This could be due to migration of counts from pre1=0 to pre1>0
in pp2006 data due to more material budget than it is Monte-Carlo
For pre1=0 condition pp2006 data shapes are not reproduced by gamma-jet Monte-Carlo.
With a larger cluster size (2x1 -> 3x3) the pp2006 and MC gamma-jet shapes
are getting closer to each other.
For pre1>0 condition (with statistics available),
pp2006 data shapes are consistent with QCD Monte-Carlo.

Cluster energy fraction in 0.7 radius: detector eta < 1.5

Energy fraction in NxN cluster within r=0.7 radius: detector eta < 1.5

Figure 1a: 2x1/0.7 energy fraction [number of counts per given fraction]

Figure 2a: 2x2/0.7 energy fraction [number of counts per given fraction]

Figure 3a: 3x3/0.7 energy fraction [number of counts per given fraction]

Yield vs. NxN cluster energy fraction in r=0.7: detector eta < 1.5

Figure 4a: 2x1/0.7 energy fraction [yield]

Figure 5a: 2x2/0.7 energy fraction [yield]

Figure 6a: 3x3/0.7 energy fraction [yield]

Cluster energy fraction in 0.7 radius: detector eta > 1.5

Energy fraction in NxN cluster within r=0.7 radius: detector eta < 1.5

Figure 1a: 2x1/0.7 energy fraction [number of counts per given fraction]

Figure 2a: 2x2/0.7 energy fraction [number of counts per given fraction]

Figure 3a: 3x3/0.7 energy fraction [number of counts per given fraction]

Yield vs. NxN cluster energy fraction in r=0.7: detector eta < 1.5

Figure 4a: 2x1/0.7 energy fraction [yield]

Figure 5a: 2x2/0.7 energy fraction [yield]

Figure 6a: 3x3/0.7 energy fraction [yield]

Cluster energy fraction in 3x3 patch: detector eta < 1.5

Energy fraction from NxN cluster in 3x3 patch: detector eta < 1.5

Figure 1a: 2x1/3x3 energy fraction [number of counts per given fraction]

Figure 2a: 2x2/3x3 energy fraction [number of counts per given fraction]

Yield vs. NxN cluster energy fraction in 3x3 patch: detector eta < 1.5

Figure 4a: 2x1/3x3 energy fraction [yield]

Figure 5a: 2x2/3x3 energy fraction [yield]

Cluster energy fraction in 3x3 patch: detector eta > 1.5

Energy fraction from NxN cluster in 3x3 patch: detector eta > 1.5

Figure 1a: 2x1/3x3 energy fraction [number of counts per given fraction]

Figure 2a: 2x2/3x3 energy fraction [number of counts per given fraction]

Yield vs. NxN cluster energy fraction in 3x3 patch: detector eta > 1.5

Figure 4a: 2x1/3x3 energy fraction [yield]

Figure 5a: 2x2/3x3 energy fraction [yield]

2008.11.25 Yiled vs. analysis cuts: eta dependence

Ilya Selyuzhenkov November 25, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.
QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.

Some observation

Fig. 1 [upper&lower left, 3rd bin] indicates that
cluster energy isolation is the most important cut
for signal/background separation
Fig.1 [lower right, 3rd bin] shows that
R_cluster cut is independent from (or orthogonal to) other cuts
Fig.1 [upper&lower left 4th bin] shows that
cut on neutral energy fraction for the away side jet
rejects more signal that background events
We probably need to reconsider that cut
Fig.2 [lower left, 5th bin] shows that
charge particle veto significantly improves
signal to background ratio

Fig.2 [lower right, 5th bin] shows that
charge particle veto also independent from other cuts

Fig.3 [lower left, 5th bin] shows that
in the region were we do not have TPC tracking (photon eta > 1.5)
charge particle veto is not efficient,
although there is still some improvement from this cut.
This probably due to tracks with eta <1.5
which fall into large isolation radius r=0.7.

Yield vs. various analysis cuts

List of cuts (sorted according to bin number in Figs. 1-3. [No SMD sided residual cuts]):

N_events : total number of di-jet events found by the jet-finder
cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy
R_{3x3cluster}>0.9 for Fig. 1, and it is disabled in Fig. 2 and 3
R_EM^jet < 0.9 : neutral energy fraction cut for on away side jet
N_ch=0 : no charge tracks associated with a gamma candidate
N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak
N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak
gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster

Figure 1: Number of accepted events vs. various analysis cuts
The starting number of events (shown in first bin of the plots) is
the number of di-jets with reconstructed gamma_pt>7 GeV and jet_pt>5 GeV
upper left: cuts applied independently
upper right: expept this cut fired
(event passed all other cuts and being rejected by this cut)
lower left: "cuts applied independently" normalized by the total number of events
lower right: "expept this cut fired" normalized by the total number of events

Figure 2: Same as Fig.1 except: no R_cluster cut and photon detector eta < 1.5
(eta region where we do have most of the TPC tracking)

Figure 3: Same as Fig.1 except: no R_cluster cut and photon detector eta > 1.5

12 Dec

December 2008 posts

2008.12.08 Run 8 EEMC QA

Ilya Selyuzhenkov December 08, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
Trigger: eemc-http-mb-L2gamma [id:137641]
pp2008 - STAR 2008 pp data
Trigger: etot-mb-l2 [id:7]
Days: 53-70; ~0.5M triggered events (1/3 of available statistics)

Detector subsystems involved in analysis:

TPC (vertex, jets, charge particle veto)
Endcap EMC (triggering, photon candidate reconstruction)
Barrel EMC (away side jet reconstruction)

Gamma-jet analysis cuts:

Select only di-jet events
cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
R_EM^jet < 0.9 : neutral energy fraction cut for the away side jet
N_ch=0 : no charge tracks associated with a gamma candidate
N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak
N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak
gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster
R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy (not applied here)

Figure 1: EEMC x vs. y position of photon candidate for 2008 data sample
Problem with pre-shower layer in Sector 10 can been seen in the upper left corner

Figure 2: EEMC x vs. y position of photon candidate for 2006 data sample

Figure 3: Average < E_pre1 * E_pre2 > for 3x3 cluster around high tower
vs run number for sectors 9, 10 and 11
Note, zero pre-shower energy for sector 10 (black points) for days 61, 62, 64, and 67.
All di-jet events for pp2008 data are shown (no gamma-jet cuts)

Figure 3a: Same as Fig.3, zoom into day 61

Figure 3b: Same as Fig.3, zoom into day 62
Figure 3c: Same as Fig.3, zoom into day 64
Figure 3d: Same as Fig.3, zoom into day 67

Figure 4: EEMC x vs. y position of photon candidate for 2008 data sample
Same as Fig. 1, but excluding days: 61, 62, 64, and 67

Conclusion on QA:

No problem with pp2008 data have been found,
except that for some runs (mostly on days 61, 62, 64, and 67)
EEMC pre-shower layer for sector 10 was off.

Comparison between 2006 and 2008 data

Figure 5: Vertex z distribution:
All gamma-jet cuts applied, plus pt_gamma>7 and pt_jet > 5 GeV (exlcuding days 61, 62, 64, and 67)
Results are shown for pp2008 data sample (black), vs. pp2006 data (red).
pp2008 data scaled to the same total number of candidates as in pp2006 data.

Figure 6: Shower shapes within +/- 30 strips from high strip (same cuts as in Fig. 5):

Figure 7: Shower shapes within +/- 5 strips from high strip
(same cuts as in Fig. 5, no scaling):

2008.12.09 pp Run 8 vs. Run 6 SMD shower shapes

Ilya Selyuzhenkov December 09, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
Trigger: eemc-http-mb-L2gamma [id:137641]
pp2008 - STAR 2008 pp data
Trigger: etot-mb-l2 [id:7]
Days: 53-70; ~0.5M triggered events (1/3 of available statistics)

2008.12.09 pp Run 8 vs. Run 6 SMD shower shapes

Ilya Selyuzhenkov December 09, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
Trigger: eemc-http-mb-L2gamma [id:137641]
pp2008 - STAR 2008 pp data
Trigger: etot-mb-l2 [id:7]
Days: 53-70; ~0.5M triggered events (1/3 of available statistics)

Gamma-jet analysis cuts:

Select only di-jet events
cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
R_EM^jet < 0.9 : neutral energy fraction cut for the away side jet
N_ch=0 : no charge tracks associated with a gamma candidate
N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak
N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak
gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster
R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy (not applied here)

Comparison between 2006 and 2008 data

Figure 1: Vertex z distribution:
All gamma-jet cuts applied, plus pt_gamma>7 and pt_jet > 5 GeV (exlcuding days 61, 62, 64, and 67)
Results are shown for pp2008 data sample (black), vs. pp2006 data (red).
pp2008 data scaled to the same total number of candidates as in pp2006 data.

Figure 2: Shower shapes within +/- 30 strips from high strip (same cuts as in Fig. 1):

Figure 3: Shower shapes within +/- 5 strips from high strip
(same cuts as in Fig. 1, no scaling):

2008.12.09 pp Run 8 vs. Run 6 SMD shower shapes

Ilya Selyuzhenkov December 09, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
Trigger: eemc-http-mb-L2gamma [id:137641]
pp2008 - STAR 2008 pp data
Trigger: etot-mb-l2 [id:7]
Days: 53-70; ~0.5M triggered events (1/3 of available statistics)

Gamma-jet analysis cuts:

Select only di-jet events
cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
R_EM^jet < 0.9 : neutral energy fraction cut for the away side jet
N_ch=0 : no charge tracks associated with a gamma candidate
N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak
N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak
gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster
R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy (not applied here)

Comparison between 2006 and 2008 data

Figure 2: Shower shapes within +/- 30 strips from high strip (same cuts as in Fig. 1):

Figure 3: Shower shapes within +/- 5 strips from high strip
(same cuts as in Fig. 1, no scaling):

2008.12.09 pp Run 8 vs. Run 6 SMD shower shapes

Ilya Selyuzhenkov December 09, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
Trigger: eemc-http-mb-L2gamma [id:137641]
pp2008 - STAR 2008 pp data
Trigger: etot-mb-l2 [id:7]
Days: 53-70; ~0.5M triggered events (1/3 of available statistics)

Gamma-jet analysis cuts:

Select only di-jet events
cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
R_EM^jet < 0.9 : neutral energy fraction cut for the away side jet
N_ch=0 : no charge tracks associated with a gamma candidate
N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak
N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak
gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster
R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy (not applied here)

Comparison between 2006 and 2008 data

Figure 2: Shower shapes within +/- 30 strips from high strip (same cuts as in Fig. 1):

Figure 3: Shower shapes within +/- 5 strips from high strip
(same cuts as in Fig. 1, no scaling):

2008.12.09 pp Run 8 vs. Run 6 SMD shower shapes

Ilya Selyuzhenkov December 09, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
Trigger: eemc-http-mb-L2gamma [id:137641]
pp2008 - STAR 2008 pp data
Trigger: etot-mb-l2 [id:7]
Days: 53-70; ~0.5M triggered events (1/3 of available statistics)

Gamma-jet analysis cuts:

Select only di-jet events
cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
R_EM^jet < 0.9 : neutral energy fraction cut for the away side jet
N_ch=0 : no charge tracks associated with a gamma candidate
N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak
N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak
gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster
R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy (not applied here)

Comparison between 2006 and 2008 data

Figure 2: Shower shapes within +/- 30 strips from high strip (same cuts as in Fig. 1):

Figure 3: Shower shapes within +/- 5 strips from high strip
(same cuts as in Fig. 1, no scaling):

Conclusion:

2008.12.09 pp Run 8 vs. Run 6 shower shapes

Ilya Selyuzhenkov December 09, 2008

Data sets:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
Trigger: eemc-http-mb-L2gamma [id:137641]
pp2008 - STAR 2008 pp data
Trigger: etot-mb-l2 [id:7]
Days: 53-70; ~0.5M triggered events (1/3 of available statistics)

Gamma-jet analysis cuts:

Select only di-jet events
cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
R_EM^jet < 0.9 : neutral energy fraction cut for the away side jet
N_ch=0 : no charge tracks associated with a gamma candidate
N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak
N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak
gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster
R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy (not applied here)

Comparison between 2006 and 2008 data

Figure 2: Shower shapes within +/- 30 strips from high strip (same cuts as in Fig. 1):

Figure 3: Shower shapes within +/- 5 strips from high strip
(same cuts as in Fig. 1, no scaling):

2008.12.11 Run 8 EEMC QA (presentation for Spin PWG)

Ilya Selyuzhenkov December 11, 2008

Run 8 QA with EEMC gamma-jet candidates

Presentation in pdf or open office file format

2008.12.16 Effect of L2gamma trigger in simulations

Ilya Selyuzhenkov December 16, 2008

Data sets

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
Trigger: eemc-http-mb-L2gamma [id:137641]
gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.
QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.

Cuts applied

Gamma-jet isolation cuts except 3x3/r=0.7 energy isolation cut

Data driven shower shape replacement maker fix

Figure 1: (reproducing old results with dd-maker fix)
transverse momentum and vertex z distributions
before (with ideal gains/pedestals) and
after (with realistic gains/pedestal tables) dd-maker fix are in a good agreement.
For details on "dd-maker problem", read these hyper news threads:
emc2:2905, emc2:2900, and phana:294
Now we can run L2gamma trigger emulation and Eemc SMD ddMaker
in the same analysis chain.

l2-gamma trigger effect in simulation

Figure 2: Vertex z distribution with and without trigger condition in simulations
(emulated trigger: eemc-http-mb-L2gamma [id:137641]).
Solid red/green symbols show results with l2gamma condition applied,
while red/green lines show results for the same analysis cuts but without trigger condition.
Note, good agreement between MC QCD jets with trigger condition on (green solid squared)
and pp2006 data (black solid circles) for pre-shower1>0 case.

Figure 3: pt distribution with/without trigger condition in simulations.
Same color coding as in Fig. 2

Figure 4: Same as Fig. 3 just on a log scale
One can clearly see large trigger effect when applied for QCD jet events,
and a little effect for direct gammas.

Figure 5: gamma candidate pt QCD (right) and prompt photon (left) Monte-Carlo:
no (upper) with (lower) L2e-gamma trigger condition
No photon pt and no jet pt cuts

Figure 6: gamma candidate pt for QCD Monte-Carlo: no L2e-gamma trigger condition
No photon pt and no jet pt cuts

Figure 7: gamma candidate pt for QCD Monte-Carlo: L2e-gamma trigger condition applied (id:137641)
No photon pt and no jet pt cuts

2008.12.19 Parton pt distribution for Pythia QCD and gamma-jet events

Ilya Selyuzhenkov December 19, 2008

Data sets

gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.
QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.

Cuts applied

Gamma-jet isolation cuts except 3x3/r=0.7 energy isolation cut

Figure 1: Parton pt distibution for gamma-jet candidates from Pythia QCD sample
with various pt and l2gamma trigger conditions

Figure 2: Parton pt distibution for gamma-jet candidates from Pythia prompt photon sample
with various pt and l2gamma trigger conditions

2009

Year 2009 posts

01 Jan

January 2009 posts

2009.01.08 Away side jet pt vs. photon pt

Ilya Selyuzhenkov January 08, 2009

Data sets

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
Trigger: eemc-http-mb-L2gamma [id:137641]
gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.
QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.

Cuts applied

Di-jet events
Require to reconstruct photon momentum (no gamma-jet isolation cuts)
Gamma pt > 7GeV
L2gamma emulation in Monte-Carlo
L2gamma triggered pp2006 events

Comments

(concentrated on pre-shower1>0 case
which has better statistics for QCD Monte-Carlo):

Fig.1, lower plots
Vertex z distributions from QCD MC and pp2006 data are different in the negative region
Fig.2, lower plots
For the away side jet pt < 8GeV region
QCD Monte-Carlo underestimates the data.
Fig.4, lower plots
gamma-jet pt asymmetry plot shows
that in QCD MC photon and jet pt's are better correlated than in the data
Fig.5, lower plots
Most of the differences between QCD MC and pp2006 data for pre-shower1>0 case
are probably from the lower gamma and jet pt region

Figures

Figure 1: Vertex z distribution

Figure 2: Away side jet pt

Figure 3: Photon pt

Figure 4: gamma-jet pt asymmetry: (pt_gamma - pt_jet)/pt_gamma

Figure 5: gamma pt vs. away side jet pt
1st column: triggered pp2006 data
2nd column: gamma-jet MC (l2gamma trigger on)
3rd column: QCD background MC (l2gamma trigger on)

2009.01.20 Away side jet pt vs. photon pt: more stats for QCD pt_parton 9-15GeV

Ilya Selyuzhenkov January 20, 2009

Note:
this is an update with 10x more statitstics for QCD 9-15GeV parton pt bin.
See this post for old results.

Data sets

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
Trigger: eemc-http-mb-L2gamma [id:137641]
gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.
QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.

Cuts applied

Di-jet events
Require to reconstruct photon momentum (no gamma-jet isolation cuts)
Gamma pt > 7GeV
L2gamma emulation in Monte-Carlo
L2gamma triggered pp2006 events

Comments

Vertex z distributions from QCD MC and pp2006 data
are different in the negative region (see Fig.1)
pp2006 data to Monte-Carlo ratio
does not depends on reconstructed photon pt,
but it has some vertex z dependence
(see data to MC ratio in Fig.6 for pre-shower1 > 4MeV case)

Figures

Figure 1: Vertex z distribution

Figure 2: Away side jet pt

Figure 3: Photon pt

Figure 4: gamma-jet pt asymmetry: (pt_gamma - pt_jet)/pt_gamma

Figure 5: gamma pt vs. away side jet pt
1st column: triggered pp2006 data
2nd column: gamma-jet MC (l2gamma trigger on)
3rd column: QCD background MC (l2gamma trigger on)

Data to Monte_Carlo normalization

Figure 6: pp2006 data to Monte -Carlo sum [QCD + gamma-jet] ratio
for pre-shower1>4MeV (most of statistics)
Left: data to MC ratio vs. reconstructed gamma pt.
Solid line shows constant line fit (p0 ~ 1.3)
Right: data to MC ratio vs. reconstructed vertex position

2009.01.27 gamma and jet pt plots with detector |eta|_jet < 0.8, pt_jet > 7

Ilya Selyuzhenkov January 27, 2009

Data sets

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
Trigger: eemc-http-mb-L2gamma [id:137641]
gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.
QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.

Cuts applied

Di-jet events
Require to reconstruct photon momentum (no gamma-jet isolation cuts)
jet pt > 7GeV
Gamma pt > 7GeV or no pt cuts
L2gamma emulation in Monte-Carlo
L2gamma triggered pp2006 events
cos (phi_jet - phi_gamma) < -0.8
detector |eta_jet|< 0.8
|v_z| < 100

Figures

All figures:

All pre-shower conditions combined, pre1<10MeV
Left plots: no gamma pt cut
Right plots: pt_gamma >7GeV
Thick blue line shows MC sum: QCD + gamma-jet
Thin solid color lines shows distributions from various partonic pt bins for QCD MC
See figures legend for color coding

Figure 1: Vertex z distribution

Figure 2: Photon eta

Figure 3: Away side jet eta

Figure 4:Photon pt

Figure 5: Away side jet pt

Figure 6: Away side jet detector eta

2009.01.27 gamma and jet pt plots with |eta|_jet < 0.7

Ilya Selyuzhenkov January 27, 2009

Data sets

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
Trigger: eemc-http-mb-L2gamma [id:137641]
gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.
QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.

Cuts applied

Di-jet events
Require to reconstruct photon momentum (no gamma-jet isolation cuts)
Gamma pt > 7GeV or no pt cuts
L2gamma emulation in Monte-Carlo
L2gamma triggered pp2006 events
cos (phi_jet - phi_gamma) < -0.8
|eta_jet|< 0.7
|v_z| < 100

Figures

All figures:

All pre-shower conditions combined, pre1<10MeV
Left plots: no gamma pt cut
Right plots: pt_gamma >7GeV
Thick blue line shows MC sum: QCD + gamma-jet
Thin solid color lines shows distributions from various partonic pt bins for QCD MC
See figures legend for color coding

Figure 1: Vertex z distribution

Figure 2: Photon eta

Figure 3: Away side jet eta

Figure 4:Photon pt

Same as in Fig.4 on a log scale: no gamma pt cut and pt>7GeV

Figure 5: Away side jet pt

Same as in Fig.5 on a log scale: no gamma pt cut and pt>7GeV

02 Feb

February 2009 posts

2009.02.02 No pre-shower cuts, Normalization fudge factor 1.24

Ilya Selyuzhenkov February 02, 2009

Data sets

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
Trigger: eemc-http-mb-L2gamma [id:137641]
gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.
QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.

Cuts applied

Di-jet events
Require to reconstruct photon momentum (no gamma-jet isolation cuts)
Gamma pt > 7GeV
L2gamma emulation in Monte-Carlo
L2gamma triggered pp2006 events
cos (phi_jet - phi_gamma) < -0.8
detector |eta_jet|< 0.8
|v_z| < 100

Figures

All figures:

All pre-shower conditions combined, No pre-shower cuts
Thick blue line shows MC sum: QCD + gamma-jet
Black solid circles: pp2006 data
Monte-Carlo results first scaled to 3.164 pb^-1 according to Pythia luminosity
and then an additional fudge factor of 1.24 has been applied.
Fudge factor is defined as the yields ratio from data to scaled with Pythia luminosity Monte-Carlo
for pt_jet>7GeV and pt_gamma>7 candidates

Figure 1: Vertex z distribution with pt_jet>7 cut (left) and without pt_jet cut (rigth)

Figure 2: Photon (left) and away side jet (right) pt

Figure 3: Photon detector eta (left) and corrected for vertex eta (right)

Figure 4: Away side jet detector eta (left) and corrected for vertex eta (right)

Figure 5: Preshower 1 (left) and Pre-shower2 (right) energy

2009.02.03 No pre-shower cuts, pt_jet >7 vs. No pt_jet cuts

Ilya Selyuzhenkov February 03, 2009

Data sets

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
Trigger: eemc-http-mb-L2gamma [id:137641]
gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.
QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events.
Partonic pt range 2-25 GeV.

Cuts applied

Di-jet events
Require to reconstruct photon momentum (no gamma-jet isolation cuts)
Gamma pt > 7GeV
L2gamma emulation in Monte-Carlo
L2gamma triggered pp2006 events
cos (phi_jet - phi_gamma) < -0.8
detector |eta_jet|< 0.8
|v_z| < 100

Figures

Each figure has:

All pre-shower conditions combined, No pre-shower cuts
Thick blue line shows MC sum: QCD + gamma-jet
Black solid circles shows pp2006 data
Left plots: pt_jet>7GeV
Right plots: no cuts on pt_jet
Monte-Carlo results for QCD and gamma-jet samples are first
scaled to 3.164 pb^-1 according to Pythia luminosity,
added together, and then an additional fudge factor of 1.24 applied.
Fudge factor is defined as pp2006 to Monte-Carlo sum ratio
for pt_jet>7GeV and pt_gamma>7 candidates

Figure 1: Vertex z distribution

Figure 2: Photon detector eta

Figure 3: Corrected for vetrex photon eta

Figure 4: Away side jet detector eta

Figure 5: Corrected for vetrex away side jet eta

Figure 6:Photon pt

Figure 7: Away side jet pt

Figure 8: Pre-shower 1 energy

Figure 9: Pre-shower 2 energy

2009.02.06 Pre-shower energy distribution Run6 vs. Run8 geometry

Ilya Selyuzhenkov February 06, 2009

Data sets

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
Trigger: eemc-http-mb-L2gamma [id:137641]
mc2006: gamma-jet+QCD jets [p6410EemcGammaFilter] events.

Partonic pt range 2-25 GeV.

pp2008 - STAR 2008 pp data
Trigger: etot-mb-l2 [id:7]

Cuts applied

Di-jet events
Require to reconstruct photon momentum (no gamma-jet isolation cuts)
Gamma pt > 7GeV, jet pt > 7GeV
L2gamma emulation in Monte-Carlo
L2gamma triggered for pp2006 and pp2008 events
cos (phi_jet - phi_gamma) < -0.8
detector |eta_jet|< 0.8
|v_z| < 100

Figures

Each figure has:

All pre-shower conditions combined, No pre-shower cuts
Red circles show pp2006 data
Black triangles show pp2008 data
Data scaled to match the integraled yield from pp2006 data
Green line shows MC sum: QCD + gamma-jet
Monte-Carlo results for QCD and gamma-jet samples are first
scaled to 3.164 pb^-1 according to Pythia luminosity,
added together, and then an additional fudge factor of 1.24 applied.
Fudge factor is defined as pp2006 to Monte-Carlo sum ratio
for pt_jet>7GeV and pt_gamma>7 candidates

Observations

Pre-shower energy distributions from pp2008 data set
are narrower than that for pp2006 data.
This corresponds to smaller amount of material budget in y2008 STAR geometry.
Pre-shower energy distribution from Monte-Carlo with y2006 geometry
closer follows the distribution from pp2008 data set, rather than that from pp2006 data.
This indicates the lack of material budget in y2006 Monte-Carlo.

Note: There is a "pre-shower sector 10 problem" for pp2008 data,
which results in migration of small fraction of events with pre-shower>0 into
pre-shower=0 bin (first zero bins in Fig.1 and 2. below).
For pre-shower>0 case this only affects overall normalization of pp2008 data,
but not the shape of pre-shower energy distributions.
I'm running jet-finder+my software to get more statistics from pp2008 data set,
and after more QA will produce list of runs with "pre-shower sector 10 problem",
so to exclude them in the next iteration of my plots.

Figure 1: Pre-shower1 energy distribution

Figure 2: Pre-shower2 energy distribution

2009.02.09 pp2006, pp2008, amd mc2006 comparison

Ilya Selyuzhenkov February 06, 2009

Data sets

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
Trigger: eemc-http-mb-L2gamma [id:137641]
mc2006: gamma-jet+QCD jets [p6410EemcGammaFilter] events.

Partonic pt range 2-25 GeV.

pp2008 - STAR 2008 pp data
Trigger: etot-mb-l2 [id:7]

Cuts applied

Di-jet events
Require to reconstruct photon momentum (no gamma-jet isolation cuts)
Gamma pt > 7GeV, jet pt > 7GeV
L2gamma emulation in Monte-Carlo
L2gamma triggered for pp2006 and pp2008 events
cos (phi_jet - phi_gamma) < -0.8
detector |eta_jet|< 0.8
|v_z| < 100

Figures

Each figure has:

All pre-shower conditions combined, No pre-shower cuts
Red circles show pp2006 data
Black triangles show pp2008 data
Data scaled to match the integraled yield from pp2006 data
Green line shows MC sum: QCD + gamma-jet
Monte-Carlo results for QCD and gamma-jet samples are first
scaled to 3.164 pb^-1 according to Pythia luminosity,
added together, and then an additional fudge factor of 1.24 applied.
Fudge factor is defined as pp2006 to Monte-Carlo sum ratio
for pt_jet>7GeV and pt_gamma>7 candidates

Kinematics

Figure 1: vertex z

Figure 2: photon detector eta

Figure 3: jet detector eta

Figure 4: photon pt

Figure 5: jet pt

Figure 6: gamma-jet pt balance

Figure 7: Photon neutral energy fraction

Figure 8: Jet neutral energy fraction

Figure 9: cos(phi_gamma-phi_jet)

Photon candidate's 2x1, 2x2, and 3x3 tower cluser energy

Figure 10: 3x3 cluster energy

Figure 11: 2x1 cluster energy

Figure 12: 2x2 cluster energy

Number of charge tracks, Barrel and Endcap towers within r=0.7 for photon and gamma

Figure 13: Number of charged track associated with photon candidate

Figure 14: Number of Barrel towers associated with photon candidate

Figure 15: Number of Endcap towers associated with photon candidate

Jet energy composition

Figure 16: Jet energy part from Barrel towers

Figure 17: Jet energy part from charge tracks

2009.02.16 pt_jet>5GeV: pre-shower sorting with new normalization

Ilya Selyuzhenkov February 16, 2009

Data sets

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1)
Trigger: eemc-http-mb-L2gamma [id:137641]
mc2006: gamma-jet+QCD jets [p6410EemcGammaFilter] events.

Partonic pt range 2-25 GeV.

pp2008 - STAR 2008 pp data
Trigger: etot-mb-l2 [id:7]

Cuts applied

Di-jet events
Require to reconstruct photon momentum (no gamma-jet isolation cuts)
Gamma pt > 7GeV, jet pt > 7GeV
L2gamma emulation in Monte-Carlo
L2gamma triggered for pp2006 and pp2008 events
cos (phi_jet - phi_gamma) < -0.8
detector |eta_jet|< 0.8
|v_z| < 100

Figures

Each figure has:

pp2008 data scaled to match the integraled yield from pp2006 data
mc2006 stand for MC sum: QCD + gamma-jet
Monte-Carlo results for QCD and gamma-jet samples are first
scaled to 3.164 pb^-1 according to Pythia luminosity,
added together, and then an additional fudge factor of 1.24 applied.
Fudge factor is defined as pp2006 to Monte-Carlo sum ratio
for pt_jet>7GeV and pt_gamma>7 candidates

plots for pt_gamma>7GeV, pt_jet > 5GeV

All pre-shower combined: 1D distributions
All pre-shower combined: 2D correlations
Pre-shower sorting 1D distributions

2009.02.19 Photon-jet analysis status update for Spin PWG

Photon-jet analysis status update for Spin PWG (February 19, 2009)

Slides: download pdf

Previous versions: v1, v2

Link for CIPANP abstract

CIPANP 2009 abstract on photon-jet measurement

CIPANP 2009 abstract on photon-jet study

Title:
"Photon-jet coincidence measurements
in polarized pp collisions at sqrt{s}=200GeV
with the STAR Endcap Calorimeter"

Abstract: download pdf

Previous versions: v1, v2, v3, v4

Conference link: CIPANP 2009

03 Mar

March 2009 posts

2009.03.02 Application of the neural network for the cut optimization (zero try)

Multilayer perceptron (feedforward neural networks)

Multilayer perceptron (MLP) is feedforward neural networks
trained with the standard backpropagation algorithm.
They are supervised networks so they require a desired response to be trained.
They learn how to transform input data into a desired response,
so they are widely used for pattern classification.
With one or two hidden layers, they can approximate virtually any input-output map.
They have been shown to approximate the performance of optimal statistical classifiers in difficult problems.

ROOT implementation for Multilayer perceptron

TMultiLayerPerceptron class in ROOT
mlpHiggs.C example

Application for cuts optimization in the gamma-jet analysis

Netwrok structure:
r3x3, (pt_gamma-pt_jet)/pt_gamma, nCharge, bBtow, eTow2x1: 10 hidden layers: one output later

Figure 1:

Upper left: Learning curve (error vs. number of training)
Learing method is: Steepest descent with fixed step size (batch learning)
Upper right: Differences (how important are initial variableles for signal/background separation)
Lower left: Network structure (ling thinkness corresponds to relative weight value)
Lower right: Network output. Red - MC gamma-jets, blue QCD background, black pp2006 data

Figure 2: Input parameters vs. network output
Row: 1: MC QCD, 2: gamma-jet, 3 pp2006 data
Vertical axis: r3x3, (pt_gamma-pt_jet)/pt_gamma, nCharge, bBtow, eTow2x1
Horisontal axis: network output

Figure 3: Same as Fig. 2 on a linear scale

2009.03.09 Application of the LDA and MLP classifiers for the cut optimization

Cut optimization with Fisher's LDA and MLP (neural network) classifiers

ROOT implementation for LDA and MLP:

Fisher's Linear Discriminant Analysis (LDA)
Multi-Layer Perceptron (MLP) [feed-forward neural network]

Application for cuts optimization in the gamma-jet analysis

LDA configuration: default

MLP configuration:

2 hidden layers [N+1:N neural network configuration, N is number of input parameters]
Learning method: stochastic minimization (1000 learning cycles)

Input parameters (same for both LDA and MLP):

Energy fraction in 3x3 cluster within a r=0.7 radius: r3x3
Photon-jet pt balance: [pt_gamma-pt_jet]/pt_gamma
Number of charge tracks within r=0.7 around gamma candidate
Number of Endcap towers fired within r=0.7 around gamma candidate
Number of Barrel towers fired within r=0.7 around gamma candidate

Figure 1: Signal efficiency and purity, background rejection (left),
and significance: Sig/sqrt[Sig+Bg] (right) vs. LDA (upper plots) and MLP (lower plots) classifier discriminants

Figure 2:

Upper left: Rejection vs. efficiency
Upper right: Purity vs. efficiency
Lower left: Purity vs. Rejection
Lower right: Significance vs. efficiency

Figure 3: Data to Monte-Carlo comparison for LDA (upper plots) and MLP (lower plots)
Good (within ~ 10%) match between data nad Monte-Carlo
a) up to 0.8 for LDA discriminant, and b) up to -0.7 for MLP.

Figure 4: Data to Monte-Carlo comparison for input parameters
from left to right
1) pt_gamma 2) pt_jet 3) r3x3 4) gamma-jet pt balance 5) N_ch[gamma] 6) N_eTow[gamma] 7) N_bTow[gamma]
Colour coding: black pp2006 data, red gamma-jet MC, green QCD MC, blue gamma-jet+QCD

Figure 5: Data to Monte-Carlo comparison:
correlations between input variables (in the same order as in Fig. 4)
and LDA classifier discriminant (horizontal axis).
1st raw: QCD MC; 2nd: gamma-jet MC; 3rd: pp2006 data; 4th: QCD+gamma-jet MC

Figure 6: Same as Fig. 6 for MLP discriminant

2009.03.26 Endcap photon-jet update at the STAR Collaboration meeting

Endcap photon-jet update at the STAR Collaboration meeting

04 Apr

April 2009 posts

2009.04.17 WSU nuclear seminar

The STAR spin program with longitudinally polarized proton beams

2009.04.21 Adding SMD info to the LDA

Cut optimization with Fisher's LDA classifier

ROOT implementation for LDA:

Fisher's Linear Discriminant Analysis (LDA)

Application for cuts optimization in the gamma-jet analysis

LDA configuration: default

LDA input parameters:

Energy fraction in 3x3 cluster within a r=0.7 radius: r3x3
Photon-jet pt balance: [pt_gamma-pt_jet]/pt_gamma
Number of charge tracks within r=0.7 around gamma candidate
Number of Endcap towers fired within r=0.7 around gamma candidate
Number of Barrel towers fired within r=0.7 around gamma candidate

Figure 1: LDA discriminant (no SMD involved in training)

Figure 2: LDA (no SMD): Efficiency, rejection, purity vs. discriminant

Figure 3: SMD energy in 25 central strips (LDA-dsicriminant>0, no pre-shower1 cut)

Figure 4: SMD energy in 25 central strips (LDA-dsicriminant>0, pre-shower1 < 10MeV)

Figure 5: Maximum residual (LDA-dsicriminant>0, no pre-shower1 cut)

Figure 6: Maximum residual (LDA-dsicriminant>0, pre-shower1 < 10MeV)

LDA+ SMD analysis

SMD info added:
a) energy in 5 central srtips
b) maximum sided residual

Figure 7:LDA with SMD: Efficiency, rejection, purity vs. LDA discriminant

Figure 8: LDA discriminant with SMD

Figure 9: Maximum residual (SMD LDA-dsicriminant>0, pre-shower1 < 10MeV)

LDA with and without SMD comparison

Figure 10:LDA (no SMD): Efficiency, rejection, purity plots

Figure 11: LDA with SMD: Efficiency, rejection, purity plots

2009.04.28 LDA plus SMD analysis with pre-shower sorting

Cut optimization with Fisher's LDA classifier

ROOT implementation for LDA:

Fisher's Linear Discriminant Analysis (LDA)

Application for cuts optimization in the gamma-jet analysis

LDA configuration: default

LDA input parameters (includes SMD inromation of the distance from max sided residual plot):

Energy fraction in 3x3 cluster within a r=0.7 radius: r3x3
Photon-jet pt balance: [pt_gamma-pt_jet]/pt_gamma
Number of charge tracks within r=0.7 around gamma candidate
Number of Endcap towers fired within r=0.7 around gamma candidate
Number of Barrel towers fired within r=0.7 around gamma candidate
Distance to 80% cut line (see this link for more details)

The number of strips in SMD u or v planes is required to be greater than 3

Figure 1: SMD energy in 25 central strips sorted by pre-shower energy

Upper left: pre1=0, pre2=0
Upper right: pre1=0, pre2>0
Lower left: 0<4MeV
Lower right: 4<10MeV

Right plot for each pre-shower condition shows the ratio of pp2006 data to sum of the Monte-Carlo samples
Colour coding:
black pp2006 data, red gamma-jet MC, green QCD MC, blue gamma-jet+QCD
(combined plot for all pre-shoer bins can be found here)

Figure 2: SMD energy in 5 central strips sorted by pre-shower energy
(combined plot can be found here)

Figure 3: Maximum residual sorted by pre-shower energy
(combined plot can be found here)

Figure 4: LDA discriminant. Note: LDA algo trained for each pre-shower condition independently

Figure 5: LDA: Efficiency, rejection, purity vs. discriminant, sorted by pre-shower energy

Figure 6: LDA: Efficiency, rejection, purity plots sorted by pre-shower energy
For each pre-shower condition each plot has 4 figures:

u-left: rejection vs. efficiency
u-right: purity vs. efficiency
l-left: purity vs. rejection
l-right: significance (signal/sqrt{signal+background}) vs. efficiency

05 May

May 2009 posts

2009.05.03 LDA: varying pt and eta cut

Cut optimization with Fisher's LDA classifier

ROOT implementation for LDA:

Fisher's Linear Discriminant Analysis (LDA)

Application for cuts optimization in the gamma-jet analysis

LDA configuration: default

LDA input parameters Set0:

Set0:
- Energy fraction in 3x3 cluster within a r=0.7 radius:
  E_3x3/E_0.7
- Photon-jet pt balance:
  [pt_gamma-pt_jet]/pt_gamma
- Number of charge tracks within r=0.7 around gamma candidate:
  Ncharge
- Number of Endcap towersL fired within r=0.7 around gamma candidate:
  NtowBarrel
- Number of Barrel towers fired within r=0.7 around gamma candidate
  NtowEndcap
Set1:
- All from Set0
- Shower shape analysis: distance to 80% cut line:
  distance to cut line
Set2:
- All from Set1
- Energy fraction in E_2x1 and E_2x2 witin E_3x3:
  E_2x1/E_2x2 and E_2x2/E_3x3
Set3:
- All from Set2
- Energy in post-shower layer under 3x3 tower patch:
  E_post^3x3

The number of strips in SMD u or v planes is required to be greater than 3

Pre-shower sorting (energy in tiles under 3x3 tower patch):

pre1=0, pre2=0
pre1=0, pre2>0
0 < pre1 < 0.004
0.004 < pre1 < 0.01
pre1 < 0.01
pre1 >= 0.01

Photon pt and rapidity cuts:

pt>7GeV
pt>8GeV
pt>9GeV
pt>10GeV
detector eta <1.4 (pt>7GeV)
detector eta > 1.4 (pt>7GeV)

Figure 0: photon pt distribution for pre-shower1<0.01
Colour coding:
black pp2006 data, red gamma-jet MC, green QCD MC, blue gamma-jet+QCD

LDA Set0

Figure 1: LDA discriminant with Set0: Data to Monte-Carlo comparison (pt>7GeV cut)

Right plot for each pre-shower condition shows the ratio of pp2006 data to sum of the Monte-Carlo samples
Colour coding:
black pp2006 data, red gamma-jet MC, green QCD MC, blue gamma-jet+QCD

Figure 2: efficiency, purity, rejection vs. LDA discriminant (pt>7GeV cut)

Figure 3: rejection vs. efficiency

Figure 4: purity vs. efficiency

Figure 5: purity vs. rejection

LDA Set1

Figure 6: LDA discriminant with Set1: Data to Monte-Carlo comparison

Figure 7: rejection vs. efficiency

Figure 8: purity vs. efficiency

Figure 9: purity vs. rejection (click link to see the figure)

LDA Set2

Figure 10: rejection vs. efficiency (click link to see the figure)

Figure 11: purity vs. efficiency

Figure 12: purity vs. rejection (click link to see the figure)

LDA Set3

Figure 13: rejection vs. efficiency (click link to see the figure)

Figure 14: purity vs. efficiency

Figure 15: purity vs. rejection (click link to see the figure)

2009.05.04 LDA: More SMD info, 3x3 tower energy, correlation matrix

Cut optimization with Fisher's LDA classifier

ROOT implementation for LDA:

Fisher's Linear Discriminant Analysis (LDA)

Application for cuts optimization in the gamma-jet analysis

LDA configuration: default

LDA input parameters Set0:

Set4 (link for results with LDA Set0-Set3):
- Energy fraction in 3x3 cluster within a r=0.7 radius:
  E_3x3/E_0.7
- Photon-jet pt balance:
  [pt_gamma-pt_jet]/pt_gamma
- Number of charge tracks within r=0.7 around gamma candidate:
  Ncharge
- Number of Endcap towersL fired within r=0.7 around gamma candidate:
  NtowBarrel
- Number of Barrel towers fired within r=0.7 around gamma candidate
  NtowEndcap
- Shower shape analysis: distance to 80% cut line:
  distance to cut line
- Energy fraction in E_2x1 and E_2x2 witin E_3x3:
  E_2x1/E_2x2 and E_2x2/E_3x3
- Energy in post-shower layer under 3x3 tower patch:
  E_post^3x3
- Tower energy in 3x3 patch:
  E_tow^3x3
- SMD-u energy in 25 central strips:
  E_smd-u^25
- SMD-v energy in 25 central strips:
  E_smd-v^25
- SMD-v peak energy (in 5 central strips):
  E_peak

The number of strips in SMD u or v planes is required to be greater than 3

Pre-shower sorting (energy in tiles under 3x3 tower patch):

pre1=0, pre2=0
pre1=0, pre2>0
0 < pre1 < 0.004
0.004 < pre1 < 0.01
pre1 < 0.01
pre1 >= 0.01

Integrated yields per pre-shower bin:

sample	total integral	pre1=0,pre2=0	pre1=0, pre2>0	0 < pre1 < 0.004	0.004 < pre1 < 0.01	pre1 < 0.01	pre1 >= 0.01
photon-jet	2.5640e+03	3.5034e+02	5.2041e+02	5.6741e+02	5.2619e+02	1.9644e+03	5.9994e+02
QCD	5.6345e+04	1.3515e+03	4.3010e+03	1.2289e+04	1.5759e+04	3.3701e+04	2.2644e+04
pp2006	6.2811e+04	6.8000e+02	2.4310e+03	1.2195e+04	1.6766e+04	3.2072e+04	3.0739e+04

Photon pt and rapidity cuts:

pt>7GeV
pt>8GeV
pt>9GeV
pt>10GeV
detector eta <1.4 (pt>7GeV)
detector eta > 1.4 (pt>7GeV)

LDA Set4

Figure 1: LDA discriminant with Set0: Data to Monte-Carlo comparison (pt>7GeV cut)

Right plot for each pre-shower condition shows the ratio of pp2006 data to sum of the Monte-Carlo samples
Colour coding:
black pp2006 data, red gamma-jet MC, green QCD MC, blue gamma-jet+QCD

Figure 2: rejection vs. efficiency

Figure 3: purity vs. efficiency

Figure 4: purity vs. rejection

Figure 5: Correlation matrix (pt>7GeV cut)
pre1=0, pre2=0

pre1=0, pre2>0

0 < pre1 < 0.004

0.004 < pre1 < 0.01

pre1 < 0.01

pre1 >= 0.01

2009.05.06 Applying cuts on LDA: request minimum purity or efficiency

Cut optimization with Fisher's LDA classifier

For this post LDA input parameters Set4 has been used

LDA for various pre-shower bins is trained independetly,
and later results with pre-shower1<0.01 are combined.

There are a set of plots for various photon pt cuts (pt> 7, 8, 9 10 GeV)
and with different selection of cutoff for LDA
(either based on purity or efficiency).
Number in brackets shows the total yield for the sample.

Link to all plots (16 total) as a single pdf file

pt > 7GeV

Figure 1: pt > 7GeV, efficiency@70

Figure 2: pt > 7GeV, purity@35

Figure 3: pt > 7GeV, purity@40

Figure 4: pt > 7GeV, purity@25 (Note: very similar to results with efficiency@70)

pt > 9GeV

Figure 5: pt > 9GeV, efficiency@70

Figure 6: pt > 9GeV, purity@35

pt > 10GeV

Figure 7: pt > 10GeV, efficiency@70

Figure 8: pt > 10GeV, purity@40

2009.05.07 Photon-jets analysis with the Endcap Calorimeter

Photon-jets with the Endcap Calorimeter

(analysis status update for Spin PWG)

Slides in pdf format:

2009.05.12 Variable distributions after LDA at 70% efficiency

Cut optimization with Fisher's LDA classifier

For this post LDA results with Set1 and Set2 has been used
Note, that LDA for various pre-shower bins is trained independetly

pdf-links with results for pre1=0 and pre2=0 (pre-shower bin 1):

before LDA cut (figures are not sorted)
after LDA cut @70% efficiency for Set 2 (figures are not sorted)

Figures below are for 0.004<pre-shower1<0.01 (pre-shower bin 4).

Photon pt cut: pt> 7, pre-shower bin: 0.004 < pre1 < 0.01
LDA cut with efficiency @ 70%

Set1 vs. Set2

What is added in Set2 compared to Set1:
smaller cluster size information (r2x1, r2x2), post-shower energy

Figure 1: r2x1
before LDA cut

LDA cut for Set1

LDA cut for Set2

Figure 2: r2x2
before LDA cut

LDA cut for Set1

LDA cut for Set2

Figure 3: r3x3
before LDA cut

LDA cut for Set1

LDA cut for Set2

Figure 4: Residual distance
before LDA cut

LDA cut for Set1

LDA cut for Set2

Other variables with LDA Set2 cut

Note: Only plos for LDA cut @70 efficiency for Set2 are shown

Figure : number of charge particles around photon

Figure 5: number of EEMC tower around photon

Figure 6: number of BEMC tower around photon

Figure 7: photon-jet pt balance

Figure 8: SMD energy in 5 centrapl strips

Figure 9: SMD energy in 25 central strips: u and v plane separately (plot for V plane)

Figure 10: 2x1 cluster energy

Figure 11: 2x2 cluster energy

Figure 12: 3x3 cluster energy

Figure 13: tower energy in r=0.7 radius

Figure 14: 3x3 pre-shower1 energy

Figure 15: 3x3 pre-shower2 energy

Figure 16: 3x3 post-shower energy

Figure 17: photon pt

Figure 18: jet pt

Figure 19: z vertex

2009.05.31 CIPANP 2009 photon-jet presentation

CIPANP 2009 presentation on photon-jet study

Title:
"Photon-jet coincidence measurements
in polarized pp collisions at sqrt{s}=200GeV
with the STAR Endcap Calorimeter"

Presentation in pdf format
Older versions: 0, 1, 2 3, 4, 5, open office file: odp
Submitted abstract
Conference link: CIPANP 2009

06 Jun

June 2009 posts

2009.06.22 CIPANP 2009 photon-jet proceedings

CIPANP 2009 proceedings on photon-jet study

Title:
"Photon-jet coincidence measurements
in polarized pp collisions at sqrt{s}=200 GeV
with the STAR Endcap Calorimeter"

Proceedings: latest version
Older versions: 01 02 03 04 05
Presentation
Submitted abstract
Conference link: CIPANP 2009

07 Jul

July 2009 posts

2009.07.21 EEMC tower response in Monte-Carlo

Data set and cuts:

gamma-jet filtered Monte-Carlo
Di-jet events from the jet finder (jets threshold: 3.5 GeV)
parton pt bin 3-4 GeV (see pt_gamma distributions for various parton pt bins)
Thrown photon pseudo-rapidity: eta in [1-2] range
Requires to reconstruct photon candidate in the EEMC

Figure 1: Average ratio: pt_true / (pt_reco/1.3) vs. pt_reco (GeV/c)

Introduce 1.3 factor here to remove the effect of the fudge factor in slow simulator
Since a limited partonic pt range (3-4 GeV) is used for this study,
there is an "artificial" increase of the plotted ratio in pt_gamma > 6 GeV range
Fig. 1 reflects similar features (over a limited pt range) as those found by Hal
in his single photon study (see slide 6 of SimulationStudies.ppt presentation)

Figure 2:
Average momentum difference: pt_true - (pt_reco/1.3) vs. pt_reco (GeV/c)

Fig. 2 shows that on average in GEANT Monte-Carlo we miss ~1GeV independent on the photon pt.
EEMC detector response can be still linear even if the ratio in Fig. 1 is not flat.
Usage of fixed 1.3 (or others, like 1.25) fudge factors are not justified.
It seems that using pt-dependent fudge factor (like it is done in this Jason's study)
is also unjustified, since the same effects (flat ratio of pt_reco/pt_true ~ 1)
can be reached by subtracting 1 GeV from the cluster energy (See Fig. 3).

Figure 3: Average ratio: (pt_true -1.06) (pt_reco/1.3) vs. pt_reco (GeV/c)
Similar to Fig. 1, but with the true photon pt reduced by 1.06 GeV
Resulting true/reco pt ratio is flat in 4-6 GeV range.

Before further pursuing our efforts in tuning the tower energy response in the Monte-Carlo,
needs to address the observed energy loss difference in the fisrt layer of the BEMC/EEMC detector.
See Jason's blog post from 2009.07.16 for more details:
Comparison muon energy deposit in the 1st BEMC/EEMC layers

08 Aug

August 2009 posts

2009.08.24 Test of corrected EEMC geometry

Test of corrected EEMC geometry (bug 1618)

Monte-Carlo setup:

One particle per event (photons, electrons, and pions)
Full STAR 2006 geometry.
In Kumac file: detp geom y2006g; gexec $STAR_LIB/geometry.so
Flat in eta (1.08-2.0), phi (0,2pi), and pt (3-30 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain)
what assumes fixed sampling fraction of 0.05 (5%)

Some definitions:

Et correction factor : average p_T^thrown / E_T^{reco}.
E_T^{reco} is the total energy in the Endcap Calorimeter (from A2Emaker)
Sampling fraction: average 0.05 * Energy^{reco} / Energy^thrown.
SMD energy: average energy in all strips fired (u-plane used for this post)
Number of SMD strips fired: average total number of strips fired (u-plane used for this post)

Notations used in the plots:

Left plots: no cAir fix
Right plots: cAir-fixed
Photons: black
Electrons: red
Pions: green

Et correction

Note: compare "Left" plots with Brians old results

Figure 1a: Et correction factor vs. pt thrown

Figure 1b: Et correction factor vs. eta thrown

Figure 1c: Et correction factor vs. phi thrown

Sampling fraction

Note: compare "Right" plots with Jason results with EEMC only geometry

Figure 2a: Sampling fraction vs. pt thrown

Figure 2b: Sampling fraction vs. energy thrown

Figure 2c: Sampling fraction vs. eta thrown

Figure 2d: Sampling fraction vs. phi thrown

SMD energy

Figure 3a: SMD energy vs. energy thrown

Figure 3b: SMD energy vs. eta thrown

Number of SMD strips fired

Figure 4a: Number of SMD strips fired vs. energy thrown

Figure 4b: Number of SMD strips fired vs. eta thrown

2009.08.25 Test of corrected EEMC geometry: shower shapes

Test of corrected EEMC geometry (bug 1618)

Monte-Carlo setup is desribed here

One particle per event (photons, electrons, and pions)
Full STAR 2006 geometry.
In Kumac file: detp geom y2006g; gexec $STAR_LIB/geometry.so
Flat in eta (1.08-2.0), phi (0,2pi), and pt (3-30 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain)
what assumes fixed sampling fraction of 0.05 (5%)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Figure 1:Single photon shower shape before (red) and after (black) EEMC cAir bug fixed
pt=7-8GeV, eta=1.2-1.4 (left), eta=1.6-1.8 (right)

Figure 2: Single photon shower shape vs. data
Monte-Carlo: pt=7-10GeV, eta=1.6-1.8
data: no pre-shower1,2; pt_photon>7, pt_jet>5. no eta cuts.
(see Fig. 1 from here for other pre-shower conditions)

2009.08.27 fixed EEMC geometry: pre-shower sorted shower shapes & eta-meson comparison

Test of corrected EEMC geometry: shower shapes (bug 1618)

Monte-Carlo setup is desribed here

One particle per event (photons, electrons, and pions)
Full STAR 2006 geometry.
In Kumac file: detp geom y2006g; gexec $STAR_LIB/geometry.so
Flat in eta (1.08-2.0), phi (0,2pi), and pt (3-30 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain)
what assumes fixed sampling fraction of 0.05 (5%)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Color coding:

Black - photon (single particle/event MC)
Red - electron (single particle/event MC)
Green - neutral pion (single particle/event MC)
Blue - photons from eta-meson decay (real data)

Single particle shower shape before (left) and after (right) EEMC cAir bug fixed
Single particle kinematic cuts: pt=7-8GeV, eta=1.2-1.4
Eta-meson shower shapes (blue) taken from Fig. 1 from here of this post
All shapes are normalized to 1 at peak (central strip).

Figure 1: Pre-shower bin 0: E_pre1=0; E_pre2=0

Figure 2: Pre-shower bin 1: E_pre1=0; E_pre2>0

Figure 3: Pre-shower bin 2: E_pre1>0; E_pre1<0.004

Figure 4: Pre-shower bin 3: E_pre1>0.004; E_pre1<0.01

Shower shape ratios

Results only for corrected EEMC geometry
All shapes are divided by MC single-photon shower shape.

Figure 5a: Pre-shower bin 0: E_pre1=0; E_pre2=0

Figure 5b: Pre-shower bin 1: E_pre1=0; E_pre2>0

Figure 5c: Pre-shower bin 2: E_pre1>0; E_pre1<0.004

Figure 5d: Pre-shower bin 3: E_pre1>0.004; E_pre1<0.01

Figure 6: Single photon to eta-meson shape ratios only (with error bars):
Pre-shower bins 0 (upper-left),1 (upper-right),2 (lower-left), and 3 (lower-right)

Extracting gamma-jet cross section at forward rapidity from pp@200GeV collisions

Analysis overview

Data samples, event selection, luminosity determination
Isolating photon-jet events
- Transverse shower shape analysis
- Isolation cuts
- Cut optimization
Trigger effects study
Data to Monte-Carlo comparison/normalization and raw yields
Acceptance/efficiency corrections
Corrected yields
Background subtraction
Systematic uncertainties
Comparison with theory

Data samples, event selection, luminosity determination

Real data, and signal/background Monte-Carlo samples:

pp@200GeV collisions, STAR produnctionLong.
Trigger: eemc-http-mb-L2gamma [id:137641] (L ~ 3.164 pb^1)
Pythia prompt photon (signal) Monte-Carlo sample.
Filtered Prompt Photon p6410EemcGammaFilter.
Partonic pt range 2-25 GeV.
Pythia 2->2 hard QCD processes (background) Monte-Carlo sample.
Filtered QCD p6410EemcGammaFilter.
Partonic pt range 2-25 GeV.

Isolating photon-jet events

Shower shape analysis
- Shower shapes
- Sided residual analysis
Isolation cuts
- Energy clustering (2x1 vs. 2x2 vs. 3x3, cluster energy vs. rapidity)
- Charge particle veto
- photon and jet pt balance
- Tower multiplicity
Cut optimization with LDA.
Input variables (list can be expanded):
- Energy fraction in 3x3 cluster within a r=0.7 radius, E_3x3/E_0.7
- Photon-jet pt balance, [pt_gamma-pt_jet]/pt_gamma
- Number of charge tracks within r=0.7 around gamma candidate
- Number of Endcap towers fired within r=0.7 around gamma candidate
- Number of Barrel towers fired within r=0.7 around gamma candidate
- Shower shape analysis: distance to 80% cut line
- Energy fraction in E_2x1 and E_2x2 witin E_3x3
- Energy in post-shower layer under 3x3 tower patch
- Tower energy in 3x3 patch
- SMD-u/v energy in 25 central strips
- SMD-u/v peak energy (in 5 central strips)

Trigger effects study

No studies yet

Trigger effects vs pt
Trigger effects vs eta
What else?

Data to Monte-Carlo comparison/normalization and raw yields

Overall data to MC normalization based on vertex z distribution
Data to MC comparison of raw yield in various detector subsystems
Uncorrected yields optimized with different efficiency/purity

Acceptance/efficiency corrections

No studies yet

What needs to be studied for acceptance/efficiency effects?
Converting reconstructed photon (jet) energy/momentum to the true one
Reconstruction efficiency vs. rapidity, pt, etc

Corrected yields

No studies yet

Produce acceptance/efficiency corrected yields

Background subtraction

No studies yet

Statistical background subtraction based on Pythia+GEANT Monte-Carlo
Estimate systematic uncertainties due to background subtraction

Systematic uncertainties

No studies yet

Calorimeter energy resolution
Trigger bias
Other effects

Comparison with theory

No comparison yet

Request for pQCD calculations at forward rapidity

09 Sep

September 2009 posts

2009.09.04 Test of corrected EEMC geometry: SVT, slow-simulator on/off, pre-shower migration

Test of corrected EEMC geometry: shower shapes (bug 1618)

Monte-Carlo setup:

One particle per event (photons, electrons, pions, and eta-meson)
Full STAR 2006 geometry (with/without SVT)
In Kumac file: detp geom y2006g; gexec $STAR_LIB/geometry.so (remove SVT with SVTT_OFF option)
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and pt (6-10 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy
(with/without EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Color coding:

Photon (single particle MC)
Electron (single particle MC)
Neutral pion (single particle MC)
Eta-meson (single particle MC)
Eta-meson [pp2006 data] (single photons from eta-meson decay)

Pre-shower bins:

Ep1 = 0, Ep2 = 0 (no energy in both EEMC pre-shower layers)
Ep1 = 0, Ep2 > 0
0 < Ep1 < 4 MeV
4 < Ep1 < 10 MeV
Ep1 > 10 MeV
All pre-shower bins combined

Ep1/Ep2 is the energy deposited in the 1st/2nd EEMC pre-shower layer.
For a single particle MC it is a sum over
all pre-shower tiles in the EEMC with energy of 3 sigma above pedestal.
For eta-meson from pp2006 data the sum is over 3x3 tower patch

Shower shapes

Single particle kinematic cuts: pt=7-8GeV, eta=1.2-1.4
Eta-meson shower shapes (blue) taken from Fig. 1 from here of this post
All shapes are normalized to 1 at peak (central strip)

Figure 1: Shower shape sorted by pre-shower conditions.
cAir-Fixed EEMC geometry (NO slow simulator, WITH SVT)
Ratio plot

Figure 2: Shower shape sorted by pre-shower conditions.
cAir-Fixed EEMC geometry (NO slow simulator, NO SVT)
Ratio plot

Figure 3: Shower shape sorted by pre-shower conditions.
cAir-Fixed EEMC geometry (WITH slow simulator, WITH SVT)
Ratio plot

Figure 4: Shower shape sorted by pre-shower conditions.
Old cAir-bug EEMC geometry (NO slow simulator, WITH SVT)
Click here to see the plot

Pre-shower migration with/without SVT

Starting with a fixed (50K events) for each type of particle.
Change in number of counts for a given pre-shower bin
with different detector configuration shows pre-shower migration

Figure 5: Pre-shower migration.
cAir-Fixed EEMC geometry (WITH SVT)

Figure 6: Pre-shower migration.
cAir-Fixed EEMC geometry (WITHOUT SVT)

Sampling fraction with/without Slow-simulator

Figure 7: Sampling fraction (0.05 E_reco / E_thrown).
cAir-Fixed EEMC geometry (WITHOUT Slow-simulator)

Figure 8: Sampling fraction (0.05 E_reco / E_thrown).
cAir-Fixed EEMC geometry (WITH Slow-simulator)
Slow simulator introduce some non-linearity in the sampling fraction

Figure 9: Sampling fraction (0.05 E_reco / E_thrown).
cAir-Fixed EEMC geometry (WITHOUT SVT, WITHOUT Slow-simulator)
Click here to see the plot

Figure 10: Sampling fraction (0.05 E_reco / E_thrown).
Old cAir-bug EEMC geometry (NO slow simulator, WITH SVT)
Click here to see the plot

2009.09.11 Test of corrected EEMC geometry: LOW_EM cuts

Test of corrected EEMC geometry: SVT and LOW_EM cuts

Monte-Carlo setup:

One particle per event (only photons in this post)
Full STAR 2006 geometry (with/without SVT, LOW_EM cuts)
In Kumac file: detp geom y2006g; gexec $STAR_LIB/geometry.so (vary SVTT_OFF, LOW_EM)
LOW_EM cut definition is given at the end of this page
Throw particles flat in eta (1.2, 1.9), phi (0, 2pi), and pt (6-10 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy
(this post: no EEMC SlowSimulator)
Vertex z=0
~50K/per iteration
Non-zero energy: 3 sigma above pedestal

Color coding:

SVT, LOW_EM marked in legend as LowEM (single photon MC)
STV, no-LOW_EM marked in legend as default (single photon MC)
no-SVT, no-LOW_EM marked in legend as no-SVT (single photon MC)
photon-jet candidates [pp2006] (used data points from this post)
photons from eta-meson [pp2006]

Pre-shower bins:

Ep1 = Ep2 = 0 (no energy in both EEMC pre-shower layers)
Ep1 = 0, Ep2 > 0
0 < Ep1 < 4 MeV
4 < Ep1 < 10 MeV
Ep1 > 10 MeV
All pre-shower bins combined

Note: Ep1/Ep2 is the energy deposited in the 1st/2nd EEMC pre-shower layer.
For a single photon MC it is a sum over
all pre-shower tiles in the EEMC with energy of 3 sigma above pedestal.
For eta-meson/gamma-jet candidates from pp2006 data the sum is over 3x3 tower patch

Shower shapes

Single particle kinematic cuts: pt=7-8GeV, eta=1.2-1.4
Eta-meson shower shapes (blue) taken from Fig. 1 from here of this post
All shapes are normalized to 1 at peak (central strip)

Figure 1: Shower shape sorted by pre-shower conditions.

Figure 2: Shower shape ratio. All shapes in Fig. 1 are divided by single photon shape
for "SVT+LOW_EM" configuration (black circles in Fig. 1)

Sampling fraction

Figure 3: Sampling fraction (0.05 * E_reco/ E_thrown)

Pre/post-shower energy and migration

Figure 4: Pre-shower1 energy (all tiles)

Figure 5: Pre-shower2 energy (all tiles)

Figure 6: Post-shower energy (all tiles)

Figure 7: Pre-shower bin photon migration

Tower energy profile

Figure 8a: Energy ratio in 2x1 to 3x3 cluster
For the first 4 pre-shower bins total yield in MC is normalized to that of the data
Blue circles indicate photon-jet candidates [pp2006] (points from this post)
Same data on a linear scale

Figure 8b: Energy ratio in 2x1 to 3x3 cluster: 7 < pt < 8 and 1.2 < eta < 1.4

Figure 8c: Energy ratio in 2x1 to 3x3 cluster: 7 < pt < 8 and 1.6 < eta < 1.8

Figure 9: Average energy ratio in 2x1 to 3x3 cluster vs. thrown energy

Figure 10: Average energy ratio in 2x1 to 3x3 cluster vs. thrown energy

LOW_EM cut definition

LOW_EM option for the STAR geometry (Low cuts on Electro-Magnetic processes)
is equivalent to the following set of GEANT cuts:

CUTGAM=0.00001
CUTELE=0.00001
BCUTE =0.00001
BCUTM =0.00001
DCUTE =0.00001
DCUTM =0.00001

All these values are for kinetic energy in GeV.

Cut meaning and GEANT default values:

CUTGAM threshold for gamma transport (0.001);
CUTELE threshold for electron and positron transport (0.001);
BCUTE threshold for photons produced by electron bremsstrahlung (-1,);
BCUTM threshold for photons produced by muon bremsstrahlung (-1);
DCUTE threshold for electrons produced by electron delta-rays (-1);
DCUTM threshold for electrons produced by muon or hadron delta-rays (-1);

Some details can be found at this link and in the GEANT manual

10 Oct

October 2009 posts

2009.10.02 Jason vs. CVS EEMC geometry: sampling fraction and shower shapes

Tests with Jason geometry file (ecalgeo.g23)

Monte-Carlo setup:

One photon per event
EEMC only geometry with LOW_EM option
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and pt (6-10 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy
(no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Color coding:

Photon with Jason geometry (single particle MC)
Photon with CVS (cAir fix) geometry (single particle MC)
Eta-meson [pp2006 data] (single photons from eta-meson decay)

Sampling fraction

Figure 1: Sampling fraction vs. thrown energy (upper plot)
and vs. azimuthal angle (lower left) and rapidity (lower right)

Shower shapes

Single particle kinematic cuts: pt=7-8GeV, eta=1.2-1.4
Eta-meson shower shapes (blue) taken from Fig. 1 from here of this post
All shapes are normalized to 1 at peak (central strip)

Figure 2: Shower shapes

Shower shapes sorted by pre-shower energy

Pre-shower bins:

Ep1 = 0, Ep2 = 0 (no energy in both EEMC pre-shower layers)
Ep1 = 0, Ep2 > 0
0 < Ep1 < 4 MeV
4 < Ep1 < 10 MeV
Ep1 > 10 MeV
All pre-shower bins combined

Figure 3: Shower shapes (left) and their ratio (right)

Figure 4: Shower shape ratios

2009.10.05 Fix to the Jason geometry file

Why volume numbers has changed in Jason geometry file?

The number of nested volumes (nv),
is the total number of parent volumes for the sensitive volume
(sensitive volume is indicated by the HITS in the tree structure below).

For the Jason and CVS files this nv number seems to be the same
(see block tree structures below).
Then why volume ids id in g2t tables has changed?

The answer I found (which seems trivial to me know)
is that in the original (CVS) file ECAL
block has been instantiated (positioned) twice.
The second appearance is the prototype (East) version of the Endcap
(Original ecalgeo.g from CVS)

        if (emcg_OnOff==1 | emcg_OnOff==3) then
             Position ECAL in CAVE z=+center
        endif
        if (emcg_OnOff==2 | emcg_OnOff==3) then
             Position ECAL in CAVE z=-center ThetaZ=180
        endif

In Jason version the second appearance has been removed
(what seems natural and it should not have any effect)
(ecalgeo.g Jason edits, g23):

        IF (emcg_OnOff>0) THEN
           Create ECAL

              .....

        IF (emcg_OnOff==2 ) THEN
           Prin1
             ('East Endcap has been removed from the geometry' )
        ENDIF               EndIF! emcg_OnOff

Unfortunately, this affects the way GEANT counts nested volumes

(effectively the total number was reduced by 1, from 8 to 7)

and this is the reason why the volume numbering scheme

in g2t tables has been affected.

I propose to put back these East Endcap line back,

since in this case it will not require any additional

changes to the EEMC decoder and g2t tables.

Block tree of the geometry file

blue - added volumes in Jason file
red - G10 volume removed in Jason file
HITS - sensitive volumes

---- Jason file ----

Block definitions

Jason geometry file

Create ECAL

Block ECAL is one EMC EndCap wheel
  Create and Position EAGA AlphaZ=halfi
EndBlock

Block EAGA IS HALF OF WHEEL AIR VOLUME FORTHE ENDCAP MODULE
  Create AND Position EMSS konly='MANY'
  Create AND Position ECGH alphaz=90 kOnly='ONLY'
EndBlock

Block EMSS is the steel support of the endcap module
  Create AND Position EFLP z=zslice-center+zwidth/2
  Create AND Position ECVO z=zslice-center+zwidth/2
  Create AND Position ESHM z=zslice-center+zwidth/2 kOnly='MANY'
  Create AND Position ECVO z=zslice-center+zwidth/2
  Create AND Position ESSP z=zslice-center+zwidth/2
  Create ERCM
  Create EPSB
EndBlock

Block ECVO is one of endcap volume with megatiles and radiators
  Create AND Position EMOD alphaz=d3 ncopy=i_sector
EndBlock

Block ESHM is the shower maxsection
  Create and Position ESPL z=currentk Only='MANY'
  Create ERSM
EndBlock

Block ECGH is air gap between endcap half wheels
  Create ECHC
EndBlock

Block ECHC is steel endcap half cover
EndBlock

Block ESSP is stainless steelback plate 
EndBlock

Block EPSB IS A PROJECTILE STAINLESS STEEL BAR
EndBlock

Block ERCM is stainless steel tie rod in calorimeter sections
EndBlock

Block ERSM is stainless steel tie rod in shower max
EndBlock

Block EMOD (fsect,lsect) IS ONE MODULEOF THE EM ENDCAP
  Create AND Position ESEC z=section-curr+secwid/2
EndBlock

Block ESEC is a single em section
  Create AND Position ERAD z=length+(cell)/2+esec_deltaz
  Create AND Position EMGT z=length+(gap+cell)/2+esec_deltaz
  Create AND Position ERAD z=length+cell/2+esec_deltaz
EndBlock

Block EMGT is a 30 degree megatile
  Create AND Position EPER alphaz=myPhi
EndBlock

Block EPER is a 5 degree slice of a 30 degree megatile (subsector)
  Create and Position ETAR x=(rbot+rtop)/2ort=yzx
EndBlock

Block ETAR is a single calorimeter cell, containing scintillator, fiber router, etc...
  Create AND Position EALP y=(-megatile+emcs_alincell)/2
  Create AND Position ESCI y=(-megatile+g10)/2+emcs_alincell _
EndBlock

Block ESCI is the active scintillator (polystyrene) layer
EndBlock

Block ERAD is the lead radiator with stainless steel cladding
  Create AND Position ELED 
EndBlock

Block ELED is a lead absorber plate
EndBlock

Block EFLP is the aluminum (aluminium) front plate of the endcap
EndBlock

Block EALP is the thin aluminium plate in calorimeter cell
EndBlock

Block ESPL is the logical volume containing an SMD plane
  Create and Position EXSG alphaz=d3 ncopy=isec kOnly='MANY'
  Create and Position EXSG alphaz=d3 ort=x-y-z ncopy=isec kOnly='MANY'
  Create and Position EXSG alphaz=d3 ncopy=isec kOnly='MANY'
  Create and Position EXSG alphaz=d3 ort=x-y-z ncopy=isec kOnly='MANY'
  Create and Position EXSG alphaz=d3 ncopy=isec kOnly='MANY'
EndBlock

Block EXSG Is another logical volume... this one acutally creates the planes
  Create and Position EXPS kONLY='MANY'
  Create and Position EHMS x=xc y=yc alphaz=-45 kOnly='ONLY'
  Create and Position EBLS x=xc y=yc z=(+esmd_apex/2+esmd_back_layer/2) alphaz=-45 kOnly='ONLY'
  Create and Position EHMS x=xc y=yc alphaz=-45 ort=x-y-z kOnly='ONLY'
  Create and Position EFLS x=xc y=yc z=(-esmd_apex/2-esmd_front_layer/2) alphaz=-45 ort=x-y-z kOnly='ONLY'
EndBlock

Block EHMS defines the triangular SMD strips
Endblock! EHMS

Block EFLS is the layer of material on the front of the SMD planes
EndBlock! EFLS

Block EBLS is the layer of material on the back of the SMD planes
EndBlock! EFLS

Block EXPS is the plastic spacer in the shower maximum section
EndBlock

CVS geometry file

Create ECAL

Block ECAL is one EMC EndCap wheel
  Create and Position EAGA AlphaZ=halfi
EndBlock

Block EAGA is half of wheel air volume forthe EndCap module
  Create and Position EMSS konly='MANY'
  Create and Position ECGH AlphaZ=90 konly='ONLY'
EndBlock

Block EMSS is steel support of the EndCap module
  Create and Position EFLP z=zslice-center+slcwid/2
  Create and Position ECVO z=zslice-center+slcwid/2
  Create and Position ESHM z=zslice-center+slcwid/2
  Create and Position ECVO z=zslice-center+slcwid/2
  Create and Position ESSP z=zslice-center+slcwid/2
  Create ERCM
  Create EPSB
EndBlock

Block ECVO is one of EndCap Volume with megatiles and radiators
  Create and Position EMOD AlphaZ=d3 Ncopy=J_section
EndBlock

Block ESHM is the SHower Maxsection
  Create and Position ESPL z=current
  Create ERSM
Endblock

Block ECGH is air Gap between endcap Half wheels
  Create ECHC
EndBlock

Block ECHC is steel EndCap Half Cover
EndBlock

Block ESSP is Stainless Steelback Plate 
endblock

Block EPSB is Projectile Stainless steel Bar
endblock

Block ERCM is stainless steel tie Rod in CaloriMeter sections
endblock

Block ERSM is stainless steel tie Rod in Shower Max
endblock

Block EMOD is one moduleof the EM EndCap
  Create and Position ESEC z=section-curr+secwid/2
endblock

Block ESEC is a single EM section
  Create and Position ERAD z=len + (cell)/2
  Create and Position EMGT z=len +(gap+cell)/2
  Create and Position ERAD z=len + cell/2
Endblock

Block EMGT is a megatile EM section
  Create and Position EPER AlphaZ=(emcs_Nslices/2-isec+0.5)*dphi
Endblock

Block EPER is a EM subsection period (super layer)
  Create and Position ETAR x=(RBot+RTop)/2ORT=YZX
EndBlock

Block ETAR is one CELL of scintillator, fiber and plastic
  Create and Position EALP y=(-mgt+emcs_AlinCell)/2
  Create and Position ESCI y=(-mgt+G10)/2+emcs_AlinCell _
EndBlock

Block ESCI is the active scintillator (polystyren) layer
endblock

Block ERAD is radiator 
  Create and PositionELED 
endblock

Block ELED is lead absorber Plate 
endblock

Block EFLP is First Aluminium plate 
endblock

Block EALP is ALuminiumPlate in calorimeter cell
endblock

Block ESPL is one of the Shower maxPLanes
  Create and position EXSG AlphaZ=d3Ncopy=isec
  Create and position EXSG AlphaZ=d3Ncopy=isec
  Create and position EXGT z=msecwd AlphaZ=d3
  Create and position EXSG AlphaZ=d3 ORT=X-Y-Z Ncopy=isec
  Create and position EXGT z=-msecwd AlphaZ=d3
  Create and position EXSG AlphaZ=d3Ncopy=isec
  Create and position EXGT z=msecwd AlphaZ=d3
  Create and position EXSG AlphaZ=d3 ORT=X-Y-Z Ncopy=isec
  Create and position EXGT z=-msecwd AlphaZ=d3
Endblock

Block EXSG is the Shower maxGap for scintillator strips
  Create EHMS
endblock

Block EHMS is sHower Max Strip
Endblock

Block EXGT is the G10 layer in the Shower Max
EndBlock

Original (ecalgeo.g) file from CVS

******************************************************************************
Module ECALGEO is the EM EndCap Calorimeter GEOmetry
Created   26 jan 1996
Author    Rashid Mehdiyev
*
* Version 1.1, W.J. Llope
*               - changed sensitive medium names...
*
* Version 2.0, R.R. Mehdiyev                                  16.04.97
*               - Support walls included
*               - intercell and intermodule gaps width updated
*               - G10 layers inserted
* Version 2.1, R.R. Mehdiyev                                  23.04.97
*               - Shower Max Detector geometry added          
*               - Variable eta grid step size introduced 
* Version 2.2, R.R. Mehdiyev                                  03.12.97
*               - Eta grid corrected 
*               - Several changes in volume's dimensions
*               - Material changes in SMD
*       
* Version 3.0, O. Rogachevsky                                 28.11.99
*               - New proposal for calorimeter SN 0401
*
* Version 4.1, O.Akio                                          3 Jan 01
*               - Include forward pion detectors

* Version 5.0, O. Rogachevsky                                 20.11.01
*               - FPD is eliminated in this version
*               - More closed to proposal description
*                 of calorimeter and SMD structure
*
******************************************************************************
+CDE,AGECOM,GCONST,GCUNIT.
*
      Content    EAGA,EALP,ECAL,ECHC,ECVO,ECGH,EFLP,EHMS,
                 ELED,EMGT,EMOD,EPER,EPSB,ERAD,ERCM,ERSM,
		 ESHM,ESEC,ESCI,ESGH,ESPL,ESSP,EMSS,
		 ETAR,EXGT,EXSG
*
      Structure  EMCG { Version, int Onoff, int fillMode}

      Structure  EMCS { Type,ZOrig,ZEnd,EtaMin,EtaMax,
                        PhiMin,PhiMax,Offset,
                        Nsupsec,Nsector,Nsection,Nslices,
                        Front,AlinCell,Frplast,Bkplast,PbPlate,LamPlate,
												BckPlate,Hub,Rmshift,SMShift,GapPlt,GapCel,
                        GapSMD,SMDcentr,TieRod(2),Bckfrnt,GapHalf,Cover}
*
      Structure  EETR { Type,Etagr,Phigr,Neta,EtaBin(13)}
*
      Structure  ESEC { Isect, FPlmat, Cell, Scint, Nlayer }
*
      Structure  EMXG {Version,Sapex,Sbase,Rin,Rout,F4}
*
      Structure  EXSE {Jsect,Zshift,Sectype(6)}
*
      Integer    I_section,J_section,Ie,is,isec,i_str,Nstr,Type,ii,jj,
                 cut,fsect,lsect,ihalf,filled
*                       
      Real       center,Plate,Cell,G10,diff,halfi,
                 tan_low,tan_upp,Tanf,RBot,Rtop,Deta,etax,sq2,sq3,
                 dup,dd,d2,d3,rshift,dphi,radiator,orgkeep,endkeep
								 
*
      Real       maxcnt,msecwd,mxgten,curr,Secwid,Section,
                 curcl,EtaTop,EtaBot,slcwid,zslice,Gap,mgt,
                 xleft,xright,yleft,yright,current,
                 rth,len,p,xc,yc,xx,yy,rbotrad,
                 Rdel,dxy,ddn,ddup
                 
    Integer    N
    Parameter (N=12)
* 
    Tanf(etax) = tan(2*atan(exp(-etax)))
* 
* ----------------------------------------------------------------------------
*
* FillMode =1 only 2-5 sectors (in the first half) filled with scintillators 
* FillMode =2 all sectors filled (still only one half of one side)
* FillMode =3 both halves (ie all 12 sectors are filled)

Fill  EMCG                          ! EM EndCAp Calorimeter basic data 
      Version  = 5.0                ! Geometry version 
      OnOff    = 3                  ! Configurations 0-no, 1-west 2-east 3-both
      FillMode = 3                  ! sectors fill mode 

Fill  EMCS                          ! EM Endcap Calorimeter geometry
      Type     = 1                  ! =1 endcap, =2 fpd edcap prototype
      ZOrig    = 268.763            ! calorimeter origin in z
      ZEnd     = 310.007            ! Calorimeter end in z
      EtaMin   = 1.086              ! upper feducial eta cut 
      EtaMax   = 2.0,               ! lower feducial eta cut
      PhiMin   = -90                ! Min phi 
      PhiMax   = 90                 ! Max phi
      Offset   = 0.0                ! offset in x
      Nsupsec  = 6                  ! Number of azimuthal supersectors        
      Nsector  = 30                 ! Number of azimutal sectors (Phi granularity)
      Nslices  = 5                  ! number of phi slices in supersector
      Nsection = 4                  ! Number of readout sections
      Front    = 0.953              ! thickness of the front AL plates
      AlinCell   = 0.02             ! Aluminim plate in cell
      Frplast  = 0.015              ! Front plastic in megatile
      Bkplast  = 0.155              ! Fiber routing guides and back plastic
      Pbplate  = 0.457              ! Lead radiator thickness
      LamPlate  = 0.05              ! Laminated SS plate thickness
      BckPlate = 3.175              ! Back SS plate thickness
      Hub      = 3.81               ! thickness of EndCap hub
      Rmshift  = 2.121              ! radial shift of module
      smshift  = 0.12               ! radial shift of steel support walls
      GapPlt   = 0.3/2              ! HALF of the inter-plate gap in phi
      GapCel   = 0.03/2             ! HALF of the radial inter-cell gap
      GapSMD   = 3.400              ! space for SMD detector
      SMDcentr = 279.542            ! SMD position
      TieRod   = {160.,195}         ! Radial position of tie rods
      Bckfrnt  = 306.832            ! Backplate front Z
      GapHalf  = 0.4                ! 1/2 Gap between halves of endcap wheel
      Cover    = 0.075              ! Cover of wheel half
*      Rmshift  = 2.121              ! radial shift of module
* --------------------------------------------------------------------------
Fill EETR                      ! Eta and Phi grid values
      Type     = 1             ! =1 endcap, =2 fpd
      EtaGr    = 1.0536        ! eta_top/eta_bot tower granularity
      PhiGr    = 0.0981747     ! Phi granularity (radians)
      NEta     = 12            ! Eta granularity
      EtaBin   = {2.0,1.9008,1.8065,1.7168,1.6317,1.5507,1.4738,
                  1.4007,1.3312,1.2651,1.2023,1.1427,1.086}! Eta rapidities
*---------------------------------------------------------------------------
Fill ESEC           ! First EM section
      ISect    = 1                           ! Section number   
      Nlayer   = 1                           ! Number of Sci layers along z
      Cell     = 1.505                       ! Cell full width in z
      Scint    = 0.5                         ! Sci layer thickness
*
Fill ESEC           ! First EM section
      ISect    = 2                           ! Section number   
      Nlayer   = 1                           ! Number of Sci layers along z
      Cell     = 1.505                       ! Cell full width in z
      Scint    = 0.5                         ! Sci layer thickness
*
Fill ESEC           ! Second EM section
      ISect    = 3                           ! Section number
      Nlayer   = 4                           ! Number of Sci layers along z
      Cell     = 1.405                       ! Cell full width in z
      Scint    = 0.4                         ! Sci layer thickness
*
Fill ESEC           ! Third EM section
      ISect    = 4                           ! Section
      Nlayer   = 18                          ! Number of layers along z
      Cell     = 1.405                       ! Cell full width in z
      Scint    = 0.4                         ! Sci layer thickness
*
Fill ESEC           ! 4th EM section
      ISect    = 5                           ! Section
      Nlayer   = 1                           ! Number of  layers along z
      Cell     = 1.505                       ! Cell full width in z
      Scint    = 0.5                         ! Sci layer thickness
*----------------------------------------------------------------------------
Fill EMXG           ! EM Endcap SMD basic data
      Version   = 1                         ! Geometry version
      Sapex     = 0.7                       ! Scintillator strip apex
      Sbase     = 1.0                       ! Scintillator strip base
      Rin = 77.41                           ! inner radius of SMD plane  
      Rout = 213.922                        ! outer radius of SMD plane
      F4 = .15                              ! F4 thickness
*----------------------------------------------------------------------------
Fill EXSE           ! First SMD section
      JSect    = 1                           ! Section number
      Zshift   = -1.215                      ! Section width
      sectype  = {4,1,0,2,1,0}               ! 1-V,2-U,3-cutV,4-cutU    
*
Fill EXSE           ! Second SMD section
      JSect    = 2                           ! Section number   
      Zshift   = 0.                          ! Section width
      sectype  = {0,2,1,0,2,3}               ! 1-V,2-U,3-cutV,4-cutU    
*
Fill EXSE           ! Third SMD section
      JSect    = 3                           ! Section number   
      Zshift   = 1.215                       ! Section width
      sectype  = {1,0,2,1,0,2}               ! 1-V,2-U,3-cutV,4-cutU    

*----------------------------------------------------------------------------
*
      Use    EMCG
*
      sq3 = sqrt(3.)
      sq2 = sqrt(2.)

      prin1 emcg_version 
        ('ECALGEO version ', F4.2)

* Endcap
      USE EMCS type=1
      USE EETR type=1
      orgkeep =  emcs_ZOrig
      endkeep =  emcs_ZEnd
      if(emcg_OnOff>0) then
        diff = 0.0
        center  = (emcs_ZOrig+emcs_ZEnd)/2
        Tan_Upp = tanf(emcs_EtaMin)
        Tan_Low = tanf(emcs_EtaMax)
        rth  = sqrt(1. + Tan_Low*Tan_Low)
        rshift  = emcs_Hub * rth
        dup=emcs_Rmshift*Tan_Upp
        dd=emcs_Rmshift*rth
        d2=rshift + dd
        radiator  = emcs_Pbplate + 2*emcs_LamPlate
*       d3=emcs_Rmshift-2*emcs_smshift
        dphi = (emcs_PhiMax-emcs_PhiMin)/emcs_Nsector
        Create ECAL
        if (emcg_OnOff==1 | emcg_OnOff==3) then
             Position ECAL in CAVE z=+center
        endif
        if (emcg_OnOff==2 | emcg_OnOff==3) then
             Position ECAL in CAVE z=-center ThetaZ=180
        endif

        if(section > emcs_Zend) then
          prin0 section,emcs_Zend
          (' ECALGEO error: sum of sections exceeds maximum ',2F12.4)
        endif
        prin1 section
        (' EndCap calorimeter total depth ',F12.4)
      endif
 
      prin1
        ('ECALGEO finished')
*
* ----------------------------------------------------------------------------
Block ECAL is one EMC EndCap wheel
      Material  Air
      Medium    standard
      Attribute ECAL   seen=1 colo=7                            !  lightblue
      shape     CONE   dz=(emcs_Zend-emcs_ZOrig)/2,
                Rmn1=orgkeep*Tan_Low-d2 Rmn2=endkeep*Tan_Low-d2,
                Rmx1=orgkeep*Tan_Upp+dup Rmx2=endkeep*Tan_Upp+dup


      do ihalf=1,2
	 filled=1
	 halfi = -105 + (ihalf-1)*180
         if (ihalf=2 & emcg_FillMode<3) filled = 0	

         Create and Position EAGA  AlphaZ=halfi

      enddo
*
			
EndBlock
* ----------------------------------------------------------------------------
Block EAGA is half of wheel air volume for  the EndCap module
      Attribute EAGA      seen=1    colo=1   serial=filled           ! black
                        
      Material  Air
      shape     CONS   dz=(emcs_Zend-emcs_ZOrig)/2,
                Rmn1=orgkeep*Tan_Low-d2 Rmn2=endkeep*Tan_Low-d2,
                Rmx1=orgkeep*Tan_Upp+dup Rmx2=endkeep*Tan_Upp+dup,
                phi1=emcs_PhiMin phi2=emcs_PhiMax

        if (filled=1) then
          Create and Position EMSS  konly='MANY'
      		curr = orgkeep ; curcl = endkeep
      		Create and position ECGH  AlphaZ=90 konly='ONLY'
				endif


EndBlock

* ----------------------------------------------------------------------------
Block EMSS is steel support of the EndCap module
      Attribute EMSS      seen=1    colo=1              ! black
                        
      Material  Iron
      shape     CONS   dz=(emcs_Zend-emcs_ZOrig)/2,
                Rmn1=orgkeep*Tan_Low-d2 Rmn2=endkeep*Tan_Low-d2,
                Rmx1=orgkeep*Tan_Upp+dup Rmx2=endkeep*Tan_Upp+dup,
                phi1=emcs_PhiMin phi2=emcs_PhiMax

      zslice = emcs_ZOrig
      prin1 zslice
      (' Front Al plane starts at:  ',F12.4)
      slcwid  = emcs_Front
      Create and Position EFLP  z=zslice-center+slcwid/2
      zslice = zslice + slcwid
                        
      prin1 zslice
      (' First calorimeter starts at:  ',F12.4)

      fsect = 1; lsect = 3

			slcwid = emcs_SMDcentr - emcs_GapSMD/2 - zslice
*
       Create and Position ECVO  z=zslice-center+slcwid/2

      slcwid  = emcs_GapSMD
      zslice = emcs_SMDcentr - emcs_GapSMD/2

			prin1 section,zslice
      (' 1st calorimeter ends, SMD starts at:  ',2F10.5)

      Create and Position ESHM  z=zslice-center+slcwid/2
      zslice = zslice + slcwid

      prin1 zslice
      ('  SMD ends at:  ',F10.5)
*
      slcwid = 0
      fsect = 4; lsect = 5
      do I_section =fsect,lsect
        USE ESEC Isect=I_section  
        Slcwid  = slcwid + esec_cell*esec_Nlayer
      enddo

			slcwid = emcs_bckfrnt - zslice

*
      Create and Position ECVO  z = zslice-center+slcwid/2

      zslice = emcs_bckfrnt

			prin1 section,zslice
      (' 2nd calorimeter ends, Back plate starts at:  ',2F10.5)

      slcwid  = emcs_BckPlate
*
         Create and Position ESSP    z=zslice-center+slcwid/2
         zslice = zslice + slcwid
      prin1 zslice
      (' BackPlate ends at:  ',F10.5)

        slcwid = emcs_Zend-emcs_ZOrig
        Create ERCM

				do i_str = 1,2
					do is = 1,5
				  	xx = emcs_phimin + is*30
						yy = xx*degrad
						xc = cos(yy)*emcs_TieRod(i_str)
						yc = sin(yy)*emcs_TieRod(i_str)
        		Position ERCM z=0 x=xc y=yc  
					enddo
				enddo

        rth = orgkeep*Tan_Upp+dup + 2.5/2
				xc = (endkeep - orgkeep)*Tan_Upp
				len = .5*(endkeep + orgkeep)*Tan_Upp + dup + 2.5/2
				yc = emcs_Zend-emcs_ZOrig
				p = atan(xc/yc)/degrad

				Create EPSB
				do is = 1,6
				  xx = -75 + (is-1)*30
					yy = xx*degrad
					xc = cos(yy)*len
					yc = sin(yy)*len
        	Position EPSB x=xc y=yc  AlphaZ=xx
				enddo

EndBlock
* ----------------------------------------------------------------------------
Block ECVO is one of EndCap Volume with megatiles and radiators
      Material  Air
      Attribute ECVO   seen=1 colo=3                            ! green
      shape     CONS   dz=slcwid/2,
                Rmn1=zslice*Tan_Low-dd Rmn2=(zslice+slcwid)*Tan_Low-dd,
                Rmx1=zslice*Tan_Upp+dup Rmx2=(zslice+slcwid)*Tan_Upp+dup

      do J_section = 1,6
			if (1 < J_section < 6 | emcg_FillMode > 1)then
			 filled = 1
			else
			 filled = 0
			endif
			d3 = 75 - (J_section-1)*30
      Create and Position EMOD AlphaZ=d3   Ncopy=J_section
			enddo

*

EndBlock
* ----------------------------------------------------------------------------
Block ESHM  is the SHower Max  section
*
      Material  Air 
      Attribute ESHM   seen=1   colo=4                  !  blue
      Shape     CONS   dz=SlcWid/2,
          rmn1=zslice*Tan_Low-dd,
          rmn2=(zslice+slcwid)*Tan_Low-dd,
          rmx1=(zslice)*Tan_Upp+dup,
          rmx2=(zslice+slcwid)*Tan_Upp+dup,
          phi1=emcs_PhiMin phi2=emcs_PhiMax

      USE EMXG Version=1
      maxcnt = emcs_SMDcentr
          prin1 zslice,section,center
          (' Z start for SMD,section:  ',3F12.4)
*
        do J_section = 1,3
         USE EXSE Jsect=J_section
*
          current = exse_Zshift
          secwid  = emxg_Sapex + 2.*emxg_F4
          section = maxcnt + exse_zshift
          prin1 j_section,current,section,secwid
          (' layer, Z, width :  ',i3,3F12.4)
          rbot=section*Tan_Low
          rtop=section*Tan_Upp
          prin1 j_section,rbot,rtop
          (' layer, rbot,rtop :  ',i3,2F12.4)
          Create and position ESPL z=current
*
        end do

        Create ERSM
				do i_str = 1,2
					do is = 1,5
				  	xx = emcs_phimin + (is)*30
						yy = xx*degrad
						xc = cos(yy)*emcs_TieRod(i_str)
						yc = sin(yy)*emcs_TieRod(i_str)
        		Position ERSM z=0 x=xc y=yc  
					enddo
				enddo

Endblock
* ----------------------------------------------------------------------------
Block ECGH is air Gap between endcap Half wheels
      Material  Air
      Medium    standard
      Attribute ECGH   seen=0 colo=7                            !  lightblue
      shape     TRD1   dz=(emcs_Zend-emcs_ZOrig)/2,
                dy =(emcs_gaphalf+emcs_cover)/2,
                dx1=orgkeep*Tan_Upp+dup,
                dx2=endkeep*Tan_Upp+dup
                

      rth = emcs_GapHalf + emcs_cover
			xx=curr*Tan_Low-d2
			xleft = sqrt(xx*xx - rth*rth)
			yy=curr*Tan_Upp+dup
			xright = sqrt(yy*yy - rth*rth)
			secwid = yy - xx
			xx=curcl*Tan_Low-d2
			yleft = sqrt(xx*xx - rth*rth)
			yy=curcl*Tan_Upp+dup
			yright = sqrt(yy*yy - rth*rth)
			slcwid = yy - xx
      xx=(xleft+xright)/2
      yy=(yleft + yright)/2
			xc = yy - xx
			len = (xx+yy)/2
			yc = curcl - curr
			p = atan(xc/yc)/degrad
      rth = -(emcs_GapHalf + emcs_cover)/2
      Create  ECHC
      Position ECHC  x=len y=rth
      Position ECHC  x=-len y=rth AlphaZ=180

EndBlock
* ----------------------------------------------------------------------------
Block ECHC is steel EndCap Half Cover
      Attribute ECHC      seen=1    colo=1              ! black
                        
      Material  Iron
      shape     TRAP   dz=(curcl-curr)/2,
			          thet=p,
                bl1=secwid/2,
                tl1=secwid/2,
                bl2=slcwid/2,
                tl2=slcwid/2,
                h1=emcs_cover/2 h2=emcs_cover/2,
                phi=0  alp1=0 alp2=0
EndBlock
* ----------------------------------------------------------------------------
Block ESSP  is Stainless Steel  back Plate 
*
      Material  Iron      
      Attribute ESSP   seen=1  colo=6 fill=1    
      shape     CONS   dz=emcs_BckPlate/2,
                Rmn1=zslice*Tan_Low-dd Rmn2=(zslice+slcwid)*Tan_Low-dd,
                Rmx1=zslice*Tan_Upp+dup Rmx2=(zslice+slcwid)*Tan_Upp+dup,
                phi1=emcs_PhiMin phi2=emcs_PhiMax
endblock
* ----------------------------------------------------------------------------
Block EPSB  is Projectile Stainless steel Bar
*
      Material  Iron      
      Attribute EPSB   seen=1  colo=6 fill=1    
      shape     TRAP   dz=(emcs_Zend-emcs_ZOrig)/2,
			          thet=p,
                bl1=2.5/2,
                tl1=2.5/2,
                bl2=2.5/2,
                tl2=2.5/2,
                h1=2.0/2  h2=2.0/2,
                phi=0  alp1=0 alp2=0
endblock
* ----------------------------------------------------------------------------
Block ERCM  is stainless steel tie Rod in CaloriMeter sections
*
      Material  Iron      
      Attribute ERSM   seen=1  colo=6 fill=1    
      shape     TUBE   dz=slcwid/2,
                rmin=0,
                rmax=1.0425  !    nobody knows exactly
endblock
* ----------------------------------------------------------------------------
Block ERSM  is stainless steel tie Rod in Shower Max
*
      Material  Iron      
      Attribute ERSM   seen=1  colo=6 fill=1    
      shape     TUBE   dz=slcwid/2,
                rmin=0,
                rmax=1.0425
endblock
* ----------------------------------------------------------------------------
Block EMOD is one module  of the EM EndCap
      Attribute EMOD      seen=1    colo=3  serial=filled         ! green
      Material  Air
      Shape     CONS   dz=slcwid/2,
           phi1=emcs_PhiMin/emcs_Nsupsec,
           phi2=emcs_PhiMax/emcs_Nsupsec,
           Rmn1=zslice*Tan_Low-dd  Rmn2=(zslice+slcwid)*Tan_Low-dd,
           Rmx1=zslice*Tan_Upp+dup Rmx2=(zslice+slcwid)*Tan_Upp+dup
*
*    Running parameter 'section' contains the position of the current section
*     It should not be modified in daughters, use 'current' variable instead.
*     SecWid is used in all 'CONS' daughters to define dimensions.
*
*
        section = zslice
        curr = zslice + slcwid/2

        Do I_section =fsect,lsect

         USE ESEC Isect=I_section  
*
         Secwid  = esec_cell*esec_Nlayer
         if (I_section = 3 | I_section = 5) then   ! no last radiator 
           Secwid  = Secwid - radiator
         else if (I_section = 4) then         ! add one more radiator 
           Secwid  = Secwid - esec_cell + radiator
         endif  
         Create and position ESEC      z=section-curr+secwid/2
         section = section + secwid
* 
      enddo
endblock
* ----------------------------------------------------------------------------
Block ESEC is a single EM section
      Attribute ESEC   seen=1    colo=1 serial=filled
      Material Air
      Medium standard
*
      Shape     CONS  dz=secwid/2,  
                rmn1=(section-diff)*Tan_Low-dd,
								rmn2=(section+secwid-diff)*Tan_Low-dd,
                rmx1=(section-diff)*Tan_Upp+dup,
								rmx2=(section+secwid-diff)*Tan_Upp+dup
*
			len = -secwid/2
      current = section
			mgt = esec_scint + emcs_AlinCell _
			       + emcs_FrPlast + emcs_BkPlast
      gap = esec_cell - radiator - mgt
      prin2 I_section,section
      (' ESEC:I_section,section',i3,F12.4)

      Do is = 1,esec_Nlayer
			
* define actual  cell thickness:         
        Cell = esec_cell
				plate = radiator
*
        if (is=nint(esec_Nlayer) & (I_section = 3 | I_section = 5)) then  
         Cell = mgt + gap
         Plate=0
        else if (I_section = 4 & is = 1) then    ! radiator only
         Cell = radiator  
        endif
*                
        prin2 I_section,is,len,cell,current
        (' ESEC:I_section,is,len,cell,current  ',2i3,3F12.4)

      	if (I_section = 4 & is = 1) then       ! radiator only
			  	cell = radiator + .14
     			Create and Position    ERAD     z=len + (cell)/2
        	len = len + cell
        	current = current + cell
      	else
          cell = mgt
					if(filled = 1) then
          	Create and Position EMGT	z=len +(gap+cell)/2
            xx = current + (gap+cell)/2
            prin2 I_section,is,xx
            (' MEGA  I_section,is ',2i3,F10.4)						
					endif
        	len = len + cell + gap
        	current = current + cell + gap

      		if (Plate>0) then
				  	cell = radiator
      			Create and Position    ERAD     z=len + cell/2
          	len = len + cell
          	current = current + cell
      		end if
        end if
      end do 
Endblock
* ----------------------------------------------------------------------------
Block EMGT is a megatile EM section
      Attribute EMGT   seen=1  colo=1 
      Material Air
      Medium standard
*
      Shape     CONS  dz=mgt/2,
      rmn1=(current-diff)*Tan_Low-dd,  rmn2=(current+mgt-diff)*Tan_Low-dd,
      rmx1=(current-diff)*Tan_Upp+dup, rmx2=(current+mgt-diff)*Tan_Upp+dup

      if (I_section=1 | I_section=2 | I_section=5) then
         Call GSTPAR (ag_imed,'CUTGAM',0.00001)
         Call GSTPAR (ag_imed,'CUTELE',0.00001)
      else
         Call GSTPAR (ag_imed,'CUTGAM',0.00008)
         Call GSTPAR (ag_imed,'CUTELE',0.001)
         Call GSTPAR (ag_imed,'BCUTE',0.0001)
      end if
*
      Do isec=1,nint(emcs_Nslices)
         Create and Position EPER AlphaZ=(emcs_Nslices/2-isec+0.5)*dphi
      End Do 
Endblock
*---------------------------------------------------------------------------
Block EPER  is a EM subsection period (super layer)
*
      Material  POLYSTYREN
      Attribute EPER   seen=1  colo=1
      Shape     CONS  dz=mgt/2, 
                phi1=emcs_PhiMin/emcs_Nsector,
                phi2=+emcs_PhiMax/emcs_Nsector,
                rmn1=(current-diff)*Tan_Low-dd,
								rmn2=(current+mgt-diff)*Tan_Low-dd,
                rmx1=(current-diff)*Tan_Upp+dup,
								rmx2=(current+mgt-diff)*Tan_Upp+dup
* 
      curcl = current+mgt/2 
      Do ie = 1,nint(eetr_NEta)
        EtaBot  = eetr_EtaBin(ie)
        EtaTop  = eetr_EtaBin(ie+1)

          RBot=(curcl-diff)*Tanf(EtaBot)
*
        if(Plate > 0) then         ! Ordinary Sci layer
         RTop=min((curcl-diff)*Tanf(EtaTop), _
                    ((current-diff)*Tan_Upp+dup))
        else                     ! last Sci layer in section
         RTop=min((curcl-diff)*Tanf(EtaTop), _
                    ((current-diff)*Tan_Upp+dup))
        endif
        check RBot<RTop
*
        xx=tan(pi*emcs_PhiMax/180.0/emcs_Nsector)
        yy=cos(pi*emcs_PhiMax/180.0/emcs_Nsector)

        Create and Position  ETAR    x=(RBot+RTop)/2  ORT=YZX
        prin2 ie,EtaTop,EtaBot,rbot,rtop
        (' EPER : ie,EtaTop,EtaBot,rbot,rtop ',i3,4F12.4)
      enddo
*
EndBlock
*  - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Block ETAR is one CELL of scintillator, fiber and plastic
*
      Attribute ETAR   seen=1  colo=4                           ! blue
*     local z goes along the radius, y is the thickness
      Shape     TRD1   dy=mgt/2   dz=(RTop-RBot)/2,
           dx1=RBot*xx-emcs_GapCel/yy,
           dx2=RTop*xx-emcs_GapCel/yy
*
        Create and Position EALP          y=(-mgt+emcs_AlinCell)/2
      	G10 = esec_scint
      	Create and Position    ESCI       y=(-mgt+G10)/2+emcs_AlinCell _
				                                            +emcs_FrPlast
EndBlock
* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Block ESCI  is the active scintillator (polystyren) layer  
*
  Material  POLYSTYREN
      Material  Cpolystyren   Isvol=1
      Attribute ESCI   seen=1   colo=7   fill=0         ! lightblue
*     local z goes along the radius, y is the thickness
      Shape     TRD1   dy=esec_scint/2,
			                 dz=(RTop-RBot)/2-emcs_GapCel
      Call GSTPAR (ag_imed,'CUTGAM',0.00008)
      Call GSTPAR (ag_imed,'CUTELE',0.001)
      Call GSTPAR (ag_imed,'BCUTE',0.0001)
      Call GSTPAR (ag_imed,'CUTNEU',0.001)
      Call GSTPAR (ag_imed,'CUTHAD',0.001)
      Call GSTPAR (ag_imed,'CUTMUO',0.001)
* define Birks law parameters
      Call GSTPAR (ag_imed,'BIRK1',1.)
      Call GSTPAR (ag_imed,'BIRK2',0.013)
      Call GSTPAR (ag_imed,'BIRK3',9.6E-6)
*     
       HITS ESCI   Birk:0:(0,10)  
*                  xx:16:H(-250,250)   yy:16:(-250,250)   zz:16:(-350,350),
*                  px:16:(-100,100)    py:16:(-100,100)   pz:16:(-100,100),
*                  Slen:16:(0,1.e4)    Tof:16:(0,1.e-6)   Step:16:(0,100),
*                  none:16:         
endblock
* ----------------------------------------------------------------------------
Block ERAD  is radiator 
*
      Material  Iron
      Attribute ERAD   seen=1  colo=6 fill=1            ! violet
      Shape     CONS  dz=radiator/2, 
                rmn1=(current)*Tan_Low-dd,
								rmn2=(current+cell)*Tan_Low-dd,
                rmx1=(current)*Tan_Upp+dup,
								rmx2=(current+radiator)*Tan_Upp+dup

      		Create and Position    ELED     

endblock
* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Block ELED  is lead absorber Plate 
*
      Material  Lead
      Attribute ELED   seen=1   colo=4  fill=1
      Shape     TUBS  dz=emcs_Pbplate/2,  
                rmin=(current)*Tan_Low,
								rmax=(current+emcs_Pbplate)*Tan_Upp,

      Call GSTPAR (ag_imed,'CUTGAM',0.00008)
      Call GSTPAR (ag_imed,'CUTELE',0.001)
      Call GSTPAR (ag_imed,'BCUTE',0.0001)
      Call GSTPAR (ag_imed,'CUTNEU',0.001)
      Call GSTPAR (ag_imed,'CUTHAD',0.001)
      Call GSTPAR (ag_imed,'CUTMUO',0.001)

endblock
* ----------------------------------------------------------------------------
Block EFLP  is First Aluminium plate 
*
      Material  Aluminium
      Attribute EFLP   seen=1  colo=3 fill=1                    ! green
      shape     CONS   dz=emcs_Front/2,
                Rmn1=68.813 Rmn2=68.813,
                Rmx1=(zslice-diff)*Tan_Upp+dup,
								Rmx2=(zslice + slcwid-diff)*Tan_Upp+dup,
                phi1=emcs_PhiMin phi2=emcs_PhiMax


endblock
* ----------------------------------------------------------------------------
Block EALP  is ALuminium  Plate in calorimeter cell
*
      Material  Aluminium
      Material  StrAluminium isvol=0
      Attribute EALP   seen=1  colo=1
      Shape     TRD1   dy=emcs_AlinCell/2  dz=(RTop-RBot)/2
      Call GSTPAR (ag_imed,'CUTGAM',0.00001)
      Call GSTPAR (ag_imed,'CUTELE',0.00001)
      Call GSTPAR (ag_imed,'LOSS',1.)
      Call GSTPAR (ag_imed,'STRA',1.)
endblock
* ----------------------------------------------------------------------------
Block ESPL  is one of the Shower max  PLanes
*
      Material  Air 
      Attribute ESPL   seen=1   colo=3                  !  blue
      Shape     TUBS   dz=SecWid/2,
                rmin=section*Tan_Low-1.526,
                rmax=(section-secwid/2)*Tan_Upp+dup,
                phi1=emcs_PhiMin phi2=emcs_PhiMax

      USE EMXG Version=1
      msecwd = (emxg_Sapex+emxg_F4)/2
			
      do isec=1,6
	 cut=1
  	 d3 = 75 - (isec-1)*30
	 if (exse_sectype(isec) = 0 | (emcg_FillMode=1 & (isec=6 | isec=1))) then
 	    cut = 0
            Create and position EXSG AlphaZ=d3              Ncopy=isec
	 else if(exse_sectype(isec) = 1) then               !   V
            Create and position EXSG AlphaZ=d3              Ncopy=isec
            Create and position EXGT z=msecwd AlphaZ=d3
	 else if(exse_sectype(isec) = 2) then               !   U
            Create and position EXSG AlphaZ=d3 ORT=X-Y-Z   Ncopy=isec
            Create and position EXGT z=-msecwd AlphaZ=d3
	 else if(exse_sectype(isec) = 3) then               !  cut V
	    cut=2
            Create and position EXSG AlphaZ=d3              Ncopy=isec
            Create and position EXGT z=msecwd AlphaZ=d3
	 else if(exse_sectype(isec) = 4) then               !  cut U 
	    cut=2
            Create and position EXSG AlphaZ=d3 ORT=X-Y-Z   Ncopy=isec
            Create and position EXGT z=-msecwd AlphaZ=d3
	 endif
      enddo

Endblock
* ----------------------------------------------------------------------------
Block EXSG  is the Shower max  Gap for scintillator strips
*
      Attribute EXSG   seen=1   colo=7   serial=cut     ! black
      Material  Air   
      Shape     TUBS   dz=SecWid/2,
                rmin=section*Tan_Low-1.526,
                rmax=(section-secwid/2)*Tan_Upp+dup,
                phi1=emcs_PhiMin/emcs_Nsupsec,
                phi2=emcs_PhiMax/emcs_Nsupsec
*
      Rbot = emxg_Rin
      Rtop = emxg_Rout

      if(cut > 0) then
      if(cut = 1) then
      	Rdel = 3.938
       	Nstr = 288
			else
      	Rdel = -.475
       	Nstr = 285
			endif
			rth = .53*rdel        ! .53 --- tentatavily
    	ddn = sq3*1.713 + Rdel  
    	ddup = .5*1.846 + 1.713 
       prin2 Rbot,Rtop,Nstr
       (' EXSG: Rbot,Rtop,Nstr',2F12.4,I5)
			 mgt = emxg_Sbase + .01
    	do i_str = 1,nstr
        p = .5*(i_str-1)*mgt + 41.3655
*
        if (p <= (.5*rbot*sq3 + rth)) then
           dxy = 1.9375*sq2
           xleft = .5*sq2*p*(sq3 + 1.) - dxy
           yleft = .5*sq2*p*(sq3 - 1.) - dxy 
           yright = .5*sq2*(sqrt( rbot*rbot - p*p) - p)
           xright = sq2*p + yright
        else if ((.5*rbot*sq3  + rth) < p <= (.5*Rtop + 1.5)) then 
           prin2 i_str,p
           (' EXSG: 2 - -i_str,p:',i3,F12.4)
           dxy = 1.9375*sq2
           xleft = .5*sq2*p*(sq3 + 1.) - dxy
           yleft = .5*sq2*p*(sq3 - 1.) - dxy 
					 dxy = rdel*sq2/sq3
           yright = .5*sq2*p*(1.- 1./sq3)
           xright = sq2*p - yright - dxy
           yright = -yright - dxy
        else if (p > (.5*rtop +1.5)) then
           prin2 i_str,p
           (' EXSG: 3 - - i_str,p:',i3,F12.4)
           yleft = (sqrt(rtop*rtop - p*p) - p)/sq2
           xleft = sq2*p + yleft
					 dxy = rdel*sq2/sq3
           yright = .5*sq2*p*(1.- 1./sq3)
           xright = sq2*p - yright - dxy
           yright = -yright - dxy
           dxy = 0. 
           if ((.5*sq3*160.- ddn) < p <= (.5*sq3*160.+ ddup) ) then
             prin2 i_str,p
             (' EXSG: 4 - - i_str,p:',i3,F12.4)
						 xc = .5*(sq3*160.+1.846)
						 yc = xc - .5*sq3*1.713
           if (p > yc) then
             dxy = .5*sq2*(2/sq3*rdel + .5*sq3*1.846 +_
								   sqrt(1.713*1.713 - (p-xc)*(p-xc)))
					 else
             dxy = sq2/sq3*(p - .5*sq3* 160. + ddn)
					 endif
           else if ((.5*sq3*195.- ddn) < p <= (.5*sq3*195. + ddup) ) then
             prin2 i_str,p
             (' EXSG: 5 - - i_str,p:',i3,F12.4)
						 xc = .5*(sq3*195.+1.846)
						 yc = xc - .5*sq3*1.713
           if (p > yc) then
             dxy = .5*sq2*(2/sq3*rdel + .5*sq3*1.846 +_
								   sqrt(1.713*1.713 - (p-xc)*(p-xc)))
					 else
             dxy = sq2/sq3*(p - .5*sq3*195. + ddn)
					 endif
           endif
             xright = xright + dxy
             yright = yright + dxy
          endif

          dxy = section*Tan_Upp - Rtop
          xc = .5*(xright+xleft) + dxy
          yc = .5*(yright+yleft)
          xx = .5*sq2*(xleft+yleft)
          yy = .5*sq2*(xright+yright)
          len = xx-yy
           prin2 i_str,p,yy,xx,len,xc,yc
           (' EXSG: i_str,x,y1,y2,len,xc,yc:',i3,6F12.4)
*
       	 Create  EHMS
      	 if (mod(i_str,2) != 0 ) then                     
          	 Position EHMS  x=xc y=yc AlphaZ=-45
      	 else
          	 Position EHMS  x=xc y=yc AlphaZ=-45 ORT=X-Y-Z
      	 endif
        end do
     	 endif


*     dcut exsg z 0 0 10 0.1 0.1
*     dcut exsg y 0 10 -50 0.7 0.7

endblock
* ----------------------------------------------------------------------------
Block EHMS is  sHower Max Strip
*
      Material  POLYSTYREN
      Material  Cpolystyren   Isvol=1
      Attribute EHMS      seen=1    colo=2  serial=cut          ! red
      Shape     TRD1 dx1=0 dx2=emxg_Sbase/2 dy=len/2 dz=emxg_Sapex/2
      Call GSTPAR (ag_imed,'CUTGAM',0.00008)
      Call GSTPAR (ag_imed,'CUTELE',0.001)
      Call GSTPAR (ag_imed,'BCUTE',0.0001)
* define Birks law parameters
      Call GSTPAR (ag_imed,'BIRK1',1.)
      Call GSTPAR (ag_imed,'BIRK2',0.0130)
      Call GSTPAR (ag_imed,'BIRK3',9.6E-6)
*
       HITS EHMS     Birk:0:(0,10)  
*                     xx:16:SH(-250,250)  yy:16:(-250,250)  zz:16:(-350,350),
*                     px:16:(-100,100)    py:16:(-100,100)  pz:16:(-100,100),
*                     Slen:16:(0,1.e4)    Tof:16:(0,1.e-6)  Step:16:(0,100),
*                     none:16:            Eloss:0:(0,10)
* 
Endblock
* ----------------------------------------------------------------------------
Block EXGT  is the G10 layer in the Shower Max  
*
*     G10 is about 60% SiO2 and 40% epoxy
      Component Si    A=28.08  Z=14   W=0.6*1*28./60.
      Component O     A=16     Z=8    W=0.6*2*16./60.
      Component C     A=12     Z=6    W=0.4*8*12./174.
      Component H     A=1      Z=1    W=0.4*14*1./174.
      Component O     A=16     Z=8    W=0.4*4*16./174.
      Mixture   g10   Dens=1.7
      Attribute EXGT   seen=1   colo=7
      Shape     TUBS   dz=emxg_F4/2,
                rmin=(section-diff)*Tan_Low-1.526,
                rmax=(section+msecwd-diff)*Tan_Upp,
                phi1=emcs_PhiMin/emcs_Nsupsec,
                phi2=emcs_PhiMax/emcs_Nsupsec
      Call GSTPAR (ag_imed,'CUTGAM',0.00001)
      Call GSTPAR (ag_imed,'CUTELE',0.00001)
EndBlock
* ----------------------------------------------------------------------------
* ECAL nice views: dcut ecvo x 1       10 -5  .5 .1
*                  draw emdi 105 0 160  2 13  .2 .1
*                  draw emdi 120 180 150  1 14  .12 .12
* ---------------------------------------------------------------------------
end

ecalgeo.g geometry file (Jason edits, g23)

ecalgeo.g geometry file (Jason Webb edits, g23)

c*****************************************************************************
Module ECALGEO is the EM EndCap Calorimeter GEOmetry
c--
Created   26 jan 1996
Author    Rashid Mehdiyev
c--
c Version 1.1, W.J. Llope
c               - changed sensitive medium names...
c
c Version 2.0, R.R. Mehdiyev                                  16.04.97
c               - Support walls included
c               - intercell and intermodule gaps width updated
c               - G10 layers inserted
c Version 2.1, R.R. Mehdiyev                                  23.04.97
c               - Shower Max Detector geometry added          
c               - Variable eta grid step size introduced 
c Version 2.2, R.R. Mehdiyev                                  03.12.97
c               - Eta grid corrected 
c               - Several changes in volumes dimensions
c               - Material changes in SMD
c       
c Version 3.0, O. Rogachevsky                                 28.11.99
c               - New proposal for calorimeter SN 0401
c
c Version 4.1, O.Akio                                          3 Jan 01
c               - Include forward pion detectors
c
c Version 5.0, O. Rogachevsky                                 20.11.01
c               - FPD is eliminated in this version
c               - More closed to proposal description
c                 of calorimeter and SMD structure
c
c*****************************************************************************
+CDE,AGECOM,GCONST,GCUNIT.
*
      Content    EAGA,EALP,ECAL,ECHC,ECVO,ECGH,EFLP,EHMS,
                 ELED,EMGT,EMOD,EPER,EPSB,ERAD,ERCM,ERSM,
                 ESHM,ESEC,ESCI,ESGH,ESPL,ESSP,EMSS,ETAR,
                 EXGT,EXSG,EXPS,EFLS,EBLS

      Structure  EMCG { Version, int Onoff, int fillMode}

      Structure  EMCS { Version,Type,zorg,zend,EtaMin,EtaMax,
                        PhiMin,PhiMax,Offset,
                        Nsupsec,Nsector,Nsection,Nslices,
                        Front,AlinCell,Frplast,Bkplast,PbPlate,LamPlate,
                        BckPlate,Hub,Rmshift,SMShift,GapPlt,GapCel,
                        GapSMD,SMDcentr,TieRod(2),Bckfrnt,GapHalf,Cover,
                        Rtie,slop}

      Structure  EETR { Type,Etagr,Phigr,Neta,EtaBin(13)}

      Structure  ESEC { Isect, FPlmat, Cell, Scint, Nlayer, deltaz, Jiggle(18) }

      Structure  EMXG {Version,Sapex,Sbase,Rin,Rout,F4}

      Structure  EXSE {Jsect,Zshift,Sectype(6)}

      Structure  ESMD {Version, front_layer, back_layer, spacer_layer, base, apex }

      Integer    I_section,J_section,Ie,is,isec,istrip,Nstr,Type,ii,jj,
                 cut,fsect,lsect,ihalf,filled,i,j,k,i_sector
                       
      Real       center,Plate,Cell,G10,halfi,
                 tan_low,tan_upp,Tanf,RBot,Rtop,Deta,etax,sq2,sq3,
                 dup,dd,d2,d3,rshift,dphi,radiator
								 
      Real       maxcnt,msecwd,mxgten,curr,Secwid,Section,
                 curcl,EtaTop,EtaBot,zwidth,zslice,Gap,megatile,
                 xleft,xright,yleft,yright,current,
                 rth,length,p,xc,yc,xx,yy,rdel,dxy,ddn,ddup

      Real       myPhi
                 
      Integer    N
      Parameter (N=12)

      Tanf(etax) = tan(2*atan(exp(-etax)))
 
c--------------------------------------------------------------------------------
c                                                                            Data
c
c FillMode =1 only 2-5 sectors (in the first half) filled with scintillators 
c FillMode =2 all sectors filled (still only one half of one side)
c FillMode =3 both halves (ie all 12 sectors are filled)
c
c OnOff    =0 Do not build geometry
c OnOff    =1 Build West Endcap
c OnOff    =2 Build East Endcap (disabled)
c OnOff    =3 Build Both Endcaps (east disabled)
c
c Note: 

Fill  EMCG                          ! EM EndCAp Calorimeter basic data 
      Version  = 5.0                ! Geometry version 
      OnOff    = 3                  ! Configurations 0-no, 1-west 2-east 3-both
      FillMode = 3                  ! sectors fill mode 
c--
Fill  EMCS                          ! EM Endcap Calorimeter geometry
      Version  = 1                  ! Versioning
      Type     = 1                  ! =1 endcap, =2 fpd edcap prototype
      ZOrg     = 268.763            ! calorimeter origin in z
      ZEnd     = 310.007            ! Calorimeter end in z
      EtaMin   = 1.086              ! upper feducial eta cut 
      EtaMax   = 2.0,               ! lower feducial eta cut
      PhiMin   = -90                ! Min phi 
      PhiMax   = 90                 ! Max phi
      Offset   = 0.0                ! offset in x
      Nsupsec  = 6                  ! Number of azimuthal supersectors        
      Nsector  = 30                 ! Number of azimutal sectors (Phi granularity)
      Nslices  = 5                  ! number of phi slices in supersector
      Nsection = 4                  ! Number of readout sections
      Front    = 0.953              ! thickness of the front AL plates
      AlinCell   = 0.02             ! Aluminim plate in cell
      Frplast  = 0.015              ! Front plastic in megatile
      Bkplast  = 0.155              ! Fiber routing guides and back plastic
      Pbplate  = 0.457              ! Lead radiator thickness
      LamPlate  = 0.05              ! Laminated SS plate thickness
      BckPlate = 3.175              ! Back SS plate thickness
      Hub      = 3.81               ! thickness of EndCap hub
      Rmshift  = 2.121              ! radial shift of module
      smshift  = 0.12               ! radial shift of steel support walls
      GapPlt   = 0.3/2              ! HALF of the inter-plate gap in phi
      GapCel   = 0.03/2             ! HALF of the radial inter-cell gap
      GapSMD   = 3.400              ! space for SMD detector                << version 2 -- 3.600 >>
      SMDcentr = 279.542            ! SMD position
      TieRod   = {160.,195}         ! Radial position of tie rods
      Bckfrnt  = 306.832            ! Backplate front Z
      GapHalf  = 0.4                ! 1/2 Gap between halves of endcap wheel
      Cover    = 0.075              ! Cover of wheel half
      Rtie     = 1.0425             ! Radius of tie rod
      Slop     = 0.1400             ! Added to cell containing radiator 6 (formerly hardcoded in geom)
c--
Fill  EMCS                          ! EM Endcap Calorimeter geometry
      Version  = 2                  ! Versioning
      Type     = 1                  ! =1 endcap, =2 fpd edcap prototype
      ZOrg     = 268.763            ! calorimeter origin in z
      ZEnd     = 310.007            ! Calorimeter end in z
      EtaMin   = 1.086              ! upper feducial eta cut 
      EtaMax   = 2.0,               ! lower feducial eta cut
      PhiMin   = -90                ! Min phi 
      PhiMax   = 90                 ! Max phi
      Offset   = 0.0                ! offset in x
      Nsupsec  = 6                  ! Number of azimuthal supersectors        
      Nsector  = 30                 ! Number of azimutal sectors (Phi granularity)
      Nslices  = 5                  ! number of phi slices in supersector
      Nsection = 4                  ! Number of readout sections
      Front    = 0.953              ! thickness of the front AL plates
      AlinCell   = 0.02             ! Aluminim plate in cell
      Frplast  = 0.015              ! Front plastic in megatile
      Bkplast  = 0.155              ! Fiber routing guides and back plastic
      Pbplate  = 0.457              ! Lead radiator thickness
      LamPlate  = 0.05              ! Laminated SS plate thickness
      BckPlate = 3.175              ! Back SS plate thickness
      Hub      = 3.81               ! thickness of EndCap hub
      Rmshift  = 2.121              ! radial shift of module
      smshift  = 0.12               ! radial shift of steel support walls
      GapPlt   = 0.3/2              ! HALF of the inter-plate gap in phi
      GapCel   = 0.03/2             ! HALF of the radial inter-cell gap
      GapSMD   = 3.600              ! space for SMD detector              (* from master_geom_bmp.xls *)
      SMDcentr = 279.542            ! SMD position
      TieRod   = {160.,195}         ! Radial position of tie rods
      Bckfrnt  = 306.832            ! Backplate front Z
      GapHalf  = 0.4                ! 1/2 Gap between halves of endcap wheel
      Cover    = 0.075              ! Cover of wheel half
      Rtie     = 0.75               ! Radius of tie rod
      Slop     = 0.0000             ! Added to cell containing radiator 6 (formerly hardcoded in geom)
c--
c---------------------------------------------------------------------------
c--
c-- Supporting documentation:
c-- http://drupal.star.bnl.gov/STAR/system/files/SMD_module_stack.pdf
c--
Fill  ESMD                     ! shower maximum detector information
      Version  = 1             ! versioning information
      front_layer  = 0.161     ! thickness of front layer 
      back_layer   = 0.210     ! thickness of back layer
      base         = 1.0       ! base of the SMD strip
      apex         = 0.7       ! apex of the SMD strip
      spacer_layer = 1.2       ! spacer layer
c--
Fill EETR                      ! Eta and Phi grid values
      Type     = 1             ! =1 endcap, =2 fpd
      EtaGr    = 1.0536        ! eta_top/eta_bot tower granularity
      PhiGr    = 0.0981747     ! Phi granularity (radians)
      NEta     = 12            ! Eta granularity
      EtaBin   = {2.0,1.9008,1.8065,1.7168,1.6317,1.5507,1.4738,
                  1.4007,1.3312,1.2651,1.2023,1.1427,1.086}! Eta rapidities
c--
c---------------------------------------------------------------------------
c--
Fill ESEC        ! Preshower 1 / Radiator 1
      ISect    = 1                           ! Section number   
      Nlayer   = 1                           ! Number of Sci layers along z
      Cell     = 1.505                       ! Cell full width in z
      Scint    = 0.475                       ! Sci layer thickness (4.75mm Bicron)
      deltaz   = -0.014                      ! Amount to shift section in z to align with as-built numbers
      Jiggle   = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} ! Degrees to shift EPER in each layer
c--
c-- Note: Jiggle allows one to shift each megatile by Jiggle(i) degrees, where
c-- i indicates the layer within the section of the calorimeter.  This feature
c-- has only been crudely tested... i.e. it compiles and creates a reasonable
c-- set of pictures, but I have not verified that every scintillator shows up...
c-- There could be volume conflicts and this would need to be checked.  --JW
c--
Fill ESEC      ! Preshower 2 / Radiator 2
      ISect    = 2                           ! Section number   
      Nlayer   = 1                           ! Number of Sci layers along z
      Cell     = 1.505                       ! Cell full width in z
      Scint    = 0.475                       ! Sci layer thickness (4.75mm Bicron)
      deltaz   = -0.0182                     ! Amount to shift section in z to align with as-built numbers
      Jiggle   = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} ! Degrees to shift EPER in each layer
c--
Fill ESEC      ! Megatiles 3-6 / Radiators 3-5
      ISect    = 3                           ! Section number
      Nlayer   = 4                           ! Number of Sci layers along z
      Cell     = 1.405                       ! Cell full width in z
      Scint    = 0.4                         ! Sci layer thickness
      deltaz   = -0.0145                     ! Amount to shift section in z to align with as-built numbers
      Jiggle   = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} ! Degrees to shift EPER in each layer
c--
Fill ESEC      ! Megatiles 7-23 / Radiators 6-23
      ISect    = 4                           ! Section
      Nlayer   = 18                          ! Number of layers along z
      Cell     = 1.405                       ! Cell full width in z
      Scint    = 0.4                         ! Sci layer thickness
      deltaz   = +0.0336                     ! Amount to shift section in z to align with as-built numbers
      Jiggle   = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} ! Degrees to shift EPER in each layer
c--
Fill ESEC      ! Postshower
      ISect    = 5                           ! Section
      Nlayer   = 1                           ! Number of  layers along z
      Cell     = 1.505                       ! Cell full width in z
      Scint    = 0.5                         ! Sci layer thickness (5.0mm Kurarary)
      deltaz   = +0.036                      ! Amount to shift section in z to align with as-built numbers
      Jiggle   = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} ! Degrees to shift EPER in each layer
c--
c----------------------------------------------------------------------------
c--
Fill EMXG           ! EM Endcap SMD basic data
      Version   = 1                          ! Geometry version
      Sapex     = 0.7                        ! Scintillator strip apex
      Sbase     = 1.0                        ! Scintillator strip base
      Rin       = 77.41                      ! inner radius of SMD plane  
      Rout      = 213.922                    ! outer radius of SMD plane
      F4        = .15                        ! F4 thickness
c--
c----------------------------------------------------------------------------
c--
Fill EXSE           ! First SMD section
      JSect    = 1                           ! Section number
      Zshift   = -1.215                      ! Section width
      sectype  = {4,1,0,2,1,0}               ! 1-V,2-U,3-cutV,4-cutU    
c--
Fill EXSE           ! Second SMD section
      JSect    = 2                           ! Section number   
      Zshift   = 0.                          ! Section width
      sectype  = {0,2,1,0,2,3}               ! 1-V,2-U,3-cutV,4-cutU    
c--
Fill EXSE           ! Third SMD section
      JSect    = 3                           ! Section number   
      Zshift   = 1.215                       ! Section width
      sectype  = {1,0,2,1,0,2}               ! 1-V,2-U,3-cutV,4-cutU    
c--
c----------------------------------------------------------------------------
c--                                                                 Materials
c--
c--  PVC used in the SMD spacer layers
c--
     Component H  A=1       Z=1   W=3.0*1.0/62.453
     Component C  A=12      Z=6   W=2.0*12.0/62.453
     Component Cl A=35.453  Z=17  W=1.0*35.453/62.453
     Mixture   PVC_Spacer   Dens=1.390*(1.20/1.00)
c--
c--  Lead alloy used in the radiators
c--
     Component  Sn        A=118.710  Z=50  W=0.014
     Component  Ca        A=40.0780  Z=20  W=0.00075
     Component  Al        A=26.9815  Z=13  W=0.0003
     Component  Pb        A=207.190  Z=82  W=0.98495
     Mixture    PbAlloy   DENS=11.35
c--
c-- Stainless Steel used in various places
c--
      Component  Cr      A=51.9960  Z=24  W=0.19
      Component  Ni      A=58.6934  Z=28  W=0.09
      Component  Fe      A=55.8450  Z=26  W=0.72
      Mixture    Steel   DENS=8.03
c--
c-- Aluminized mylar.  According to information which I dug up on a google
c-- search, this is typically mylar coated with a thin (1000 angstrom) layer
c-- of aluminium on each side.
c--
c-- http://www.eljentechnology.com/datasheets/EJ590-B10HH%20data%20sheet.pdf
c--
      Component Mylar   A=12.875 Z=6.4580 w=0.999
      Component Al      A=26.980 Z=13.000 w=0.001
      Mixture   AlMylar dens=1.390
c--
c-- G10 Epoxy used in various places
c--
      Component Si    A=28.08  Z=14   W=0.6*1*28./60.
      Component O     A=16     Z=8    W=0.6*2*16./60.
      Component C     A=12     Z=6    W=0.4*8*12./174.
      Component H     A=1      Z=1    W=0.4*14*1./174.
      Component O     A=16     Z=8    W=0.4*4*16./174.
      Mixture   G10   Dens=1.7
c--
c-- Fibreglass cloth used in SMD stackup.  I googled this one too... a self-
c-- described expert quotes typical densities and percent by volume
c-- http://en.allexperts.com/q/Composite-Materials-2430/fiberglass-1.htm
c-- 
c-- glass fiber: 2.6 g/cm3 (17.6%)   resin: 1.3 g/cm3 (82.4%)
c--
c-- Fiberglass density = 1.529 g/cm3
c--
c-- I will assume that G10 epoxy is close enough to the typical resins
c-- used, at least in terms of chemical composition. Then
c--
        Component G10   A=18.017     Z=9.013    W=1.3*0.824/(1.3*0.824+2.6*0.176)
        Component Si    A=28.08      Z=14       W=2.6*0.176/(1.3*0.824+2.6*0.176)*28.08/60.08
        Component O     A=16         Z=8        W=2.6*0.176/(1.3*0.824+2.6*0.176)*32.00/60.08
        Mixture   Fiberglass         dens=1.53
c--
c--
c----------------------------------------------------------------------------
c-- Select versions of various geometry data
c--
      Use    EMCG    
      Use    EMCS   Version=2   
      Use    EETR    
c--
c----------------------------------------------------------------------------
c-- Calculate frequently used quantities
c--
      sq3 = sqrt(3.)                                ! 1/tan(30deg) = sq3
      sq2 = sqrt(2.)
c--
c--
      center  = (emcs_zorg+emcs_zend)/2             ! center of the calorimeter
      tan_upp = tanf(emcs_etamin)                   ! think this is angle pointing to top of calo
      tan_low = tanf(emcs_etamax)                   ! think this is angle pointing to bot of calo
      rth     = sqrt(1. + tan_low*tan_low)          ! ??
      rshift  = emcs_hub * rth                      ! ??
      dup     = emcs_rmshift*tan_upp                !
      dd      = emcs_rmshift*rth                    !
      d2      = rshift + dd                         !
      radiator  = emcs_pbplate + 2*emcs_lamplate    ! thickness of radiator assembly
      dphi = (emcs_phimax-emcs_phimin)/emcs_nsector ! single endcap sector
c--
c----------------------------------------------------------------------------

c----------------------------------------------------------------------------
c--                                                                     BEGIN
      Prin1 emcg_version
        ('ecalgeo version: ',F4.2) 
c--
      IF (emcg_OnOff>0) THEN
c--
c--     Build the EEMC geometry for one half wheel
c--
        Create ECAL
c--
c--     Position the two halves.  Bottom half installed in 2003, top
c--     half in 2004... so we allow logic to allow for the time
c--     evolution of the calorimeter
c--

c--
c--     West Endcap
c--
        IF (emcg_OnOff==1 | emcg_OnOff==3) THEN
           Position ECAL in CAVE z=+center
        ENDIF
        IF (section > emcs_zend) THEN
          Prin1 section, emcs_zend
            (' ECALGEO error: sum of sections exceeds maximum ',2F12.4)
        ENDIF

        IF (emcg_OnOff==2 ) THEN
           Prin1
             ('East Endcap has been removed from the geometry' )
        ENDIF
c--
      EndIF! emcg_OnOff
c--
      Prin1
        ('ECALGEO finished')

c--
c--                                                                       END
c----------------------------------------------------------------------------








c----------------------------------------------------------------- Block ECAL --
c--
Block ECAL    is one EMC EndCap wheel
c--
c-- The EEMC is built from two 180 degree half-wheels tilted at an angle
c-- with respect to zero in the STAR reference frame.  This block is serves
c-- as a logical volume which creates the two half wheels.  
c--
c-- Creates:
c-- + EAGA
c--
      Material  Air
      Attribute ECAL   seen=0 colo=7                           !  lightblue
c--
      Shape     CONE   dz=(emcs_zend-emcs_zorg)/2,
                       rmn1=emcs_zorg*tan_low-d2,
                       rmn2=emcs_zend*tan_low-d2,
                       rmx1=emcs_zorg*tan_upp+dup,
                       rmx2=emcs_zend*tan_upp+dup
c--
c--
      DO ihalf=1,2
c--
	     filled = 1
	     halfi  = -105 + (ihalf-1)*180
         if (ihalf=2 & emcg_FillMode<3) filled = 0	
c--
         Create and Position EAGA  AlphaZ=halfi
c--
      ENDDO
c--		
EndBlock




c----------------------------------------------------------------- Block EAGA --
c--
Block EAGA        IS HALF OF WHEEL AIR VOLUME FOR  THE ENDCAP MODULE
c--
c-- The eemc is divided into two halves.  one half installed for 2003 run,
c-- second half added for 2004 and beyond.  the eaga block represents one
c-- of these half-wheels.  it is an air volume which will be filled in 
c-- with additional detector components.
c--
c-- Creates:
c-- + EMSS -- steel support block
c-- + ECGH -- air gap between the two halves
c--
C--                        
      Material  AIR
      Attribute EAGA      seen=0    colo=1   serial=FILLED           ! BLACK
C--
      Shape     CONS   dz=(emcs_zend-emcs_zorg)/2,
                rmn1=emcs_zorg*tan_low-d2 rmn2=emcs_zend*tan_low-d2,
                rmx1=emcs_zorg*tan_upp+dup rmx2=emcs_zend*tan_upp+dup,
                phi1=emcs_phimin phi2=emcs_phimax
c--
c--
      IF ( FILLED .EQ. 1 ) THEN
c--
          Create AND Position EMSS konly='MANY'
c--
          curr  = emcs_zorg 
          curcl = emcs_zend
c--
          Create AND Position ECGH alphaz=90 kOnly='ONLY'
c--
      ENDIF
c--
EndBlock



c----------------------------------------------------------------- Block EMSS --
c--
Block EMSS                             is the steel support of the endcap module
c--
c-- Creates:
c--   + EFLP -- ALUMINIUM FRONT PLATE
c--   + ECVO -- VOLUMES TO CONTAIN RADIATORS AND MEGATILES
c--   + ESHM -- SHOWER MAX DETECTOR VOLUME
c--   + ESSP -- STAINLESS STEEL BACKPLATE
c--   + ERCM -- STAINLESS STEEL TIE-RODS PENETRATING ECVO
c--
c--                        
      Material  Steel
c--
      Attribute EMSS      seen=1    colo=1              ! BLACK
      Shape     CONS   dz=(emcs_zend-emcs_zorg)/2,
                rmn1=emcs_zorg*tan_low-d2 rmn2=emcs_zend*tan_low-d2,
                rmx1=emcs_zorg*tan_upp+dup rmx2=emcs_zend*tan_upp+dup,
                phi1=emcs_phimin phi2=emcs_phimax
c--
c--   Aluminium front plate 
C--
      zslice = emcs_zorg
      zwidth = emcs_front
c--
      Prin1 zslice+zwidth/2
        (' Front Al plate centered at: ', F12.4 )
c--
      Create AND Position EFLP z=zslice-center+zwidth/2
      zslice = zslice + zwidth
C--
      Prin1 zslice
         (' FIRST CALORIMETER STARTS AT:  ',F12.4)
c--
c--   Preshower 1, preshower 2, and calorimeter tiles up to
c--   megatile number six.
c--
      fsect = 1                                          ! first section 
      lsect = 3                                          ! last section
c--
      zwidth = emcs_smdcentr - emcs_gapsmd/2 - zslice    ! width of current slice
c--
      Prin1 zslice+zwidth/2
        ('Sections 1-3 positioned at: ', F12.4 )
c--
      Create AND Position ECVO  z=zslice-center+zwidth/2
c--
      zwidth  = emcs_gapsmd
      zslice  = emcs_smdcentr - emcs_gapsmd/2
c--
      Prin1 section, zslice
        (' 1st calorimeter ends, smd starts at:  ',2f10.5)
      Prin1 zwidth
        (' smd width = ',f10.5 )
c--
      Prin1 zslice+zwidth/2
         ('SMD section centered at:  ', F12.4 )
c--                                                             Do not kill neighbors
      Create AND Position ESHM  z=zslice-center+zwidth/2        kOnly='MANY'
      zslice = zslice + zwidth
c--
      Prin1 zslice
        ('  SMD ends at:  ',f10.5)
c--
c--
      fsect = 4                                             ! first section
      lsect = 5                                             ! last section
c--
c--   Calculate the width of  the last two calorimeter sections
c--
      zwidth = 0
      DO i_section = fsect,lsect
c--
        USE ESEC isect=i_section  
        zwidth  = zwidth + esec_cell*esec_nlayer
c--
      ENDDO
c--
c--   =============================================================
c--
c--   Total width will be between the back plate and the current
c--   position... this effectively turns the geometry into an
c--   accordian... whatever was defined earlier will compress
c--   / expand this section.  so correcting the smd gap will 
c--   result in some small, sub-mm shifts of radiators and 
c--   megatiles... one would like to actually place these 
c--   into their absolute positions.
c--
c--   ==============================================================
c--
      zwidth = emcs_bckfrnt - zslice
c--
      Prin1 zslice+zwidth/2
        ('Sections 4-5 positioned at: ', F12.4 )
c--
      Create AND Position ECVO  z=zslice-center+zwidth/2
c--
      zslice = emcs_bckfrnt
c--
      Prin1 section,zslice
        (' 2nd calorimeter ends, back plate starts at:  ',2f10.5)
c--
      zwidth  = emcs_bckplate
c--
      Create AND Position ESSP    z=zslice-center+zwidth/2
c--
      zslice = zslice + zwidth
c--
      Prin1 zslice
        ('EEMC Al backplate ends at: ',F12.4 )
c--
c-- Done with the calorimeter stackup.  now go back and cut through the
c-- calorimeter stack with the tie rods
c--
c--   slice width will be full calorimeter depth
      zwidth = emcs_zend-emcs_zorg
c--
      Create ERCM
c--
      DO i = 1,2               ! two tie rods along 
         DO j = 1,5            ! each gap between sectors (5 gaps)
            xx = emcs_phimin + j*30
            yy = xx*degrad
            xc = cos(yy)*emcs_tierod(i)
            yc = sin(yy)*emcs_tierod(i)
            Position ERCM z=0 x=xc y=yc  
         ENDDO
      ENDDO
c--
c--   Now add in projective steel bars which form part of the support
c--   structure of the eemc
c--
      rth = emcs_zorg*tan_upp+dup + 2.5/2
      xc = (emcs_zend - emcs_zorg)*tan_upp
      length = .5*(emcs_zend + emcs_zorg)*tan_upp + dup + 2.5/2
      yc = emcs_zend-emcs_zorg
      p = atan(xc/yc)/degrad
c--
      Create EPSB
      DO i = 1,6
c--
         xx = -75 + (i-1)*30
         yy = xx*degrad
         xc = cos(yy)*length
         yc = sin(yy)*length
c--
         Position EPSB X=XC Y=YC  ALPHAZ=XX
c--
      ENDDO
c--
EndBlock








c----------------------------------------------------------------- Block ECVO --
c--
Block ECVO                  is one of endcap volume with megatiles and radiators
c--
c-- CreateS:
c-- + EMOD -- Responsible for creating esec which, in a glorious example
c--           of spaghetti code, turns around and creates esec, which is
c--           responsible for creating the radiators before and after the
c--           smd layers.
C--
      Material  AIR
      Attribute ECVO   seen=1 colo=3                            ! GREEN
      Shape     CONS   dz=zwidth/2,
                rmn1=zslice*tan_low-dd,
                rmn2=(zslice+zwidth)*tan_low-dd,
                rmx1=zslice*tan_upp+dup,
                rmx2=(zslice+zwidth)*tan_upp+dup
c--
c--   Loop over the SIX SECTORS in the current half-wheel.  determine
c--   whether the sector is filled or not, and create the "module".
c--   By "module", we really mean endcap sector.  (Lots of code in the
c--   EEMC borrows from the barrel, and so barrel modlues get mapped
c--   to EEMC sectors).
c--
      DO i_sector = 1,6
c--
         IF (1 < I_SECTOR < 6 | EMCG_FILLMODE > 1) THEN
			 filled = 1
         ELSE
			 filled = 0
         ENDIF
c--
         d3 = 75 - (i_sector-1)*30
         Create AND Position EMOD alphaz=d3   ncopy=i_sector
c--
       ENDDO
c--
EndBlock








c----------------------------------------------------------------- Block ESHM --
c--
Block ESHM                                            is the shower max  section
c--
c-- CreateS:
c-- + ESPL -- SHOWER MAXIMUM DETECTOR PLANES
c-- + ERSM -- TIE RODS W/IN THE SHOWER MAXIMUM DETECTOR
c--
      Material  AIR 
      Attribute ESHM   seen=1   colo=4           !  BLUE
c--
      Shape     CONS   dz=zwidth/2,
                rmn1=(zslice*tan_low)-dd,
                rmn2=(zslice+zwidth)*tan_low-dd,
                rmx1=(zslice)*tan_upp+dup,
                rmx2=(zslice+zwidth)*tan_upp+dup,
                phi1=emcs_phimin phi2=emcs_phimax
c--
      USE EMXG 
c--
      maxcnt = emcs_smdcentr
      Prin1 zslice, section, center
        (' === z start for smd,section:  ',3f12.4)
c--
c--   Loop over the three possible locations for the smd planes and
c--   create them.  note that code w/in espl will decide which of
c--   5 types of smd planes are created... u, v, cutu,cutv or spacer.
c--
       DO j_section = 1,3
c--
          USE EXSE jsect=j_section
c--
          current = exse_zshift
          secwid  = emxg_sapex + 2.*emxg_f4
          section = maxcnt + exse_zshift
c--
          Prin1 j_section,current,section,secwid
            (' layer, z, width :  ',i3,3f12.4)
c--
          rbot=section*tan_low
          rtop=section*tan_upp
c--
          Prin1 j_section,rbot,rtop
            (' layer, rbot,rtop :  ',i3,2f12.4)
c--
          Prin1 j_section, center+current
            (' smd layer=',I1,' z=',F12.4 )
c--                                                           Do not kill neighbors
          Create and Position ESPL z=current                  kOnly='MANY'
c--
       ENDDO
c--
c--    Add in the tie rods which penetrate the SMD layers
c--
       Create ERSM
c--
       DO i = 1,2
		  DO j = 1,5
		  	xx = emcs_phimin + j*30
			yy = xx*degrad
			xc = cos(yy)*emcs_tierod(i)
			yc = sin(yy)*emcs_tierod(i)
            Position ERSM Z=0 X=XC Y=YC  
          END DO
       END DO
C--
EndBlock








c----------------------------------------------------------------- Block ECGH --
c--
Block ECGH                                is air gap between endcap half wheels
c--
c-- Creates:
c-- + ECHC -- THE STAINLESS STEEL COVER FOR 1/2 OF THE EEMC.
c--
      Material  AIR
      Medium    standard
      Attribute ECGH   seen=0 colo=7                            !  LIGHTBLUE
      Shape     TRD1   dz=(emcs_zend-emcs_zorg)/2,
                dy =(emcs_gaphalf+emcs_cover)/2,
                dx1=emcs_zorg*tan_upp+dup,
                dx2=emcs_zend*tan_upp+dup
c--
c--                
      rth = emcs_gaphalf + emcs_cover
      xx=curr*tan_low-d2
      xleft = sqrt(xx*xx - rth*rth)
      yy=curr*tan_upp+dup
      xright = sqrt(yy*yy - rth*rth)
      secwid = yy - xx
      xx=curcl*tan_low-d2
      yleft = sqrt(xx*xx - rth*rth)
      yy=curcl*tan_upp+dup
      yright = sqrt(yy*yy - rth*rth)
      zwidth = yy - xx
      xx=(xleft+xright)/2
      yy=(yleft + yright)/2
      xc = yy - xx
      length = (xx+yy)/2
      yc = curcl - curr
      p = atan(xc/yc)/degrad
      rth = -(emcs_gaphalf + emcs_cover)/2
c--
      Create  ECHC
c--
      Position ECHC  X=+LENGTH Y=RTH
      Position ECHC  X=-LENGTH Y=RTH ALPHAZ=180
c--
EndBlock




c----------------------------------------------------------------- Block ECHC --
c--
Block ECHC                                            is steel endcap half cover
c--
      Material  steel
      Attribute ECHC      seen=1    colo=1              ! BLACK
c--
      Shape     TRAP   dz=(curcl-curr)/2,
	            thet=p,
                bl1=secwid/2,
                tl1=secwid/2,
                bl2=zwidth/2,
                tl2=zwidth/2,
                h1=emcs_cover/2,
                h2=emcs_cover/2,
                phi=0,  
                alp1=0,
                alp2=0
c--
EndBlock



c----------------------------------------------------------------- Block ESSP --
c--
Block ESSP                                        is stainless steel  back plate 
c--
      Material  steel
      Attribute ESSP   seen=1  colo=6 fill=1    
      Shape     CONS   dz=emcs_bckplate/2,
                       rmn1=zslice*tan_low-dd,
                       rmn2=(zslice+zwidth)*tan_low-dd,
                       rmx1=zslice*tan_upp+dup,
                       rmx2=(zslice+zwidth)*tan_upp+dup,
                       phi1=emcs_phimin,
                       phi2=emcs_phimax
c--
EndBlock




c----------------------------------------------------------------- Block EPSB --
c--
Block EPSB  IS A PROJECTILE STAINLESS STEEL BAR
C--
      Material  Steel
      Attribute EPSB   seen=1  colo=6 FILL=1    
      Shape     TRAP   dz=(emcs_zend-emcs_zorg)/2,
	            thet=p,
                bl1=2.5/2,
                tl1=2.5/2,
                bl2=2.5/2,
                tl2=2.5/2,
                h1=2.0/2,
                h2=2.0/2,
                phi=0,
                alp1=0,
                alp2=0
c--
c--
EndBlock





c----------------------------------------------------------------- Block ERCM --
c--
Block ERCM                    is stainless steel tie rod in calorimeter sections
c--
      Material  Steel
      Attribute ERSM     seen=1  colo=6 FILL=1    
c--
      Shape     TUBE   dz=zwidth/2,
                rmin=0,
                rmax=emcs_rtie
c--
c-- Looks like the tie rods are meant to engage the 1.525 cm diameter holes 
c-- piercing the ears of the smd spacer... 1.5 cm may be a better approximation
c-- here.
c--
c-- http://drupal.star.bnl.gov/star/system/files/smd_spacer_drawings.pdf
c--
EndBlock






c----------------------------------------------------------------- Block ERSM --
c--
Block ERSM                             is stainless steel tie rod in shower max
c--
      Material  Steel
      Attribute ERSM       seen=1  colo=6 FILL=1    
c--
      Shape     TUBE dz=zwidth/2,
                rmin=0,
                rmax=emcs_rtie
c--
c-- see comments above
c--
EndBlock







c----------------------------------------------------------------- Block EMOD --
c--
Block EMOD   (fsect,lsect)  IS ONE MODULE  OF THE EM ENDCAP
c--
c-- Arguements: (do be defined prior to the creation of this block)
c--
c--   fsect -- first section to create
c--   lsect -- last section to create
c--
      Attribute EMOD      seen=1    colo=3  serial=FILLED         ! GREEN
      Material  Air
      Shape     CONS   dz=zwidth/2,
                phi1=emcs_phimin/emcs_nsupsec,
                phi2=emcs_phimax/emcs_nsupsec,
                rmn1=zslice*tan_low-dd,
                rmn2=(zslice+zwidth)*tan_low-dd,
                rmx1=zslice*tan_upp+dup,
                rmx2=(zslice+zwidth)*tan_upp+dup
c--
c--  Running parameter 'section' contains the position of the current section
c--   it should not be modified in daughters, use 'current' variable instead.
c--   secwid is used in all 'cons' daughters to define dimensions.
c--
        section = zslice
        curr = zslice + zwidth/2
c--
c--
        DO i_section = fsect, lsect

        USE ESEC isect=i_section  
c--
        secwid  = esec_cell*esec_nlayer
c--
c--     Section 3 precedes the smd.  section 5 is the post shower.  in
c--     both cases these sections end with a scintillator layer and no
c--     radiator.
c--
        IF (I_SECTION = 3 | I_SECTION = 5) THEN   
           secwid  = secwid - radiator
        ELSE IF (I_SECTION = 4) THEN                     ! add one more radiator 
           secwid  = secwid - esec_cell + radiator
        ENDIF
c--  
        Prin1 i_section, section-curr+secwid/2
          ('+ ECVO isection=',I1,' zcenter=', F12.4)
c--
        Create AND Position ESEC z=section-curr+secwid/2
c--
        section = section + secwid
c--
      ENDDO! Loop over sections
c--
EndBlock








c----------------------------------------------------------------- Block ESEC --
c--
Block ESEC                                              is a single em section

      Material  AIR
      Medium    standard
      Attribute ESEC seen=1 colo=1 serial=filled  lsty=2
c--
      Shape     CONS  dz=secwid/2,  
                rmn1=(section)*tan_low-dd,
                rmn2=(section+secwid)*tan_low-dd,
                rmx1=(section)*tan_upp+dup,
                rmx2=(section+secwid)*tan_upp+dup
c--
      length = -secwid/2
      current = section
c--
      megatile = esec_scint+emcs_alincell+emcs_frplast+emcs_bkplast
c--
      gap = esec_cell - radiator - megatile
      Prin2 i_section,section
        (' ESEC:i_section,section',i3,f12.4)
c--
c--   Loop over all layers in this section
c--
      DO is = 1,esec_nlayer
c--
c--	    Define actual  cell thickness:         
        cell  = esec_cell
        plate = radiator
c--
        IF (is=nint(esec_nlayer) & (i_section = 3 | i_section = 5)) THEN
c--
           cell = megatile + gap
           plate=0
c--
        ELSE IF (i_section = 4 & is = 1) THEN    ! RADIATOR ONLY
c--
           cell = radiator  
c--
        ENDIF
c--
        Prin2 i_section,is,length,cell,current
          (' esec:i_section,is,length,cell,current  ',2i3,3f12.4)
C--
C--     This handles the special case in the section after the smd.
c--     this section begins with a lead radiator.  the previous section
c--     ended with a plastic scintillator
c--
      	IF (i_section = 4 & is = 1) THEN       ! radiator only
c--
c$$$           cell = radiator + .14
           cell = radiator + emcs_slop
                          ! ^^^^ probably the fiber router layer... but is this needed here?
c--
           Prin1 is, current + cell/2+esec_deltaz
              ( '  + ESEC radiator ilayer=',I2,' z=',F12.4 )
           Create AND Position ERAD z=length+(cell)/2+esec_deltaz
c--
           length  = length + cell
           current = current + cell
c--
c--     All other cases are standard radiator followed by scintillator
c--
        ELSE
c--
           cell = megatile
           IF (FILLED = 1) THEN
c--
              Create AND Position EMGT z=length+(gap+cell)/2+esec_deltaz
c--
              xx = current + (gap+cell)/2+esec_deltaz
              prin2 i_section,is,xx
                (' mega  i_section,is ',2i3,f10.4)
              Prin1 is, xx
                 ('  + ESEC megatile ilayer=',I2,' z=',F12.4)
c--
           ENDIF 
c--
           length  = length  + cell + gap
           current = current + cell + gap
c--
           IF (PLATE>0) THEN
c--
              cell = radiator
              Prin1 is, current + cell/2+esec_deltaz
                 ( '  + ESEC radiator ilayer=',I2,' z=',F12.4 )
              Create AND Position ERAD z=length+cell/2+esec_deltaz
c--
              length  = length  + cell
          	  current = current + cell
c--
           ENDIF
c--
         ENDIF
c--
      ENDDO
c--
c--
EndBlock








c----------------------------------------------------------------- Block EMGT --
c--
Block EMGT                                               is a 30 degree megatile
c--
      Material  Air
      Medium    Standard
      Attribute EMGT   seen=1  colo=1    lsty=2
c--
      Shape     CONS  dz=megatile/2,
                rmn1=(current)*tan_low-dd,  
                rmn2=(current+megatile)*tan_low-dd,
                rmx1=(current)*tan_upp+dup, 
                rmx2=(current+megatile)*tan_upp+dup
c--
c--
      DO isec=1,nint(emcs_nslices)
c--
         myPhi = (emcs_nslices/2-isec+0.5)*dphi + esec_jiggle(is)
c--
         Create AND Position EPER alphaz=myPhi
c--
      END DO 
c--
EndBlock




c----------------------------------------------------------------- Block EPER --
c--
Block EPER               is a 5 degree slice of a 30 degree megatile (subsector)
c--
c--   Creates:
c--   + ETAR -- The pseudo-rapidity divivisions in the megatiles
c--
      Material  Polystyren
      Attribute EPER       seen=1  colo=1   lsty=1
c--
c--
c--
      Shape     CONS  dz=megatile/2, 
                phi1=emcs_phimin/emcs_nsector,
                phi2=emcs_phimax/emcs_nsector,
                rmn1=(current)*tan_low-dd,
                rmn2=(current+megatile)*tan_low-dd,
                rmx1=(current)*tan_upp+dup,
                rmx2=(current+megatile)*tan_upp+dup
c--
      curcl = current+megatile/2 
      DO ie = 1, nint(eetr_neta)
c--
        etabot  = eetr_etabin(ie)
        etatop  = eetr_etabin(ie+1)

        rbot=(curcl)*tanf(etabot)
        rtop=min((curcl)*tanf(etatop), ((current)*tan_upp+dup))
c--
        check rbot<rtop
c--
        xx=tan(pi*emcs_phimax/180.0/emcs_nsector)
        yy=cos(pi*emcs_phimax/180.0/emcs_nsector)

        Create and Position  ETAR    x=(rbot+rtop)/2  ort=yzx
        prin2 ie,etatop,etabot,rbot,rtop
          (' EPER : ie,etatop,etabot,rbot,rtop ',i3,4f12.4)
c--
      ENDDO
c--
EndBlock






c----------------------------------------------------------------- Block ETAR --
c--
c-- ETAR is a single cell of scintillator, including fiber router, plastic,
c-- etc...
c-- 
c-- local z is radially outward in star
c-- local y is the thickness of the layer
c--
Block ETAR is a single calorimeter cell, containing scintillator, fiber router, etc...
c--
      Material  POLYSTYREN
      Attribute ETAR   seen=1  colo=4  lsty=1                         ! BLUE
c--
      Shape TRD1 dy=megatile/2 dz=(rtop-rbot)/2,
            dx1=rbot*xx-emcs_gapcel/yy,
            dx2=rtop*xx-emcs_gapcel/yy
c--
        Create AND Position EALP y=(-megatile+emcs_alincell)/2
      	g10 = esec_scint
      	Create AND Position ESCI y=(-megatile+g10)/2+emcs_alincell _
				                               +emcs_frplast
c--
EndBlock







c----------------------------------------------------------------- Block ESCI --
c--
Block ESCI                        is the active scintillator (polystyrene) layer  
c--
c--   Obtain the definition of polystyrene on this line, next line clones
      Material  Polystyren 
      Material  Ecal_scint   isvol=1
      Medium    Ecal_active  isvol=1
c--
      Attribute ESCI   seen=1   colo=7   fill=0    lsty=1     ! LIGHTBLUE
c--   local z goes along the radius, y is the thickness
      Shape     TRD1   dy=esec_scint/2,
                dz=(rtop-rbot)/2-emcs_gapcel
c--
c--
      Call ecal_set_cuts( ag_imed, 'detector' )
c--
c--
      HITS ESCI   BIRK:0:(0,10)  
c--
c--
EndBlock







c----------------------------------------------------------------- Block ERAD --
c--
Block ERAD                   is the lead radiator with stainless steel cladding
c--
c-- Creates:
c-- + ELED -- the business end of the calorimeter...
c--
      Material STEEL
c--
      Attribute ERAD   seen=1  colo=6 fill=1    lsty=1        ! VIOLET
      Shape     CONS  dz=radiator/2, 
                rmn1=(current)*tan_low-dd,
                rmn2=(current+cell)*tan_low-dd,
                rmx1=(current)*tan_upp+dup,
                rmx2=(current+radiator)*tan_upp+dup
c--
      Create AND Position ELED     
c--
EndBlock
c-------------------------------------------------------------------------






c----------------------------------------------------------------- Block ELED --
c--
Block ELED                                              is a lead absorber plate
c--
c--
      Material  PbAlloy
      Medium    Ecal_lead
      Attribute ELED   seen=1 colo=4 fill=1 lsty=1
c--
      Shape     TUBS  dz=emcs_pbplate/2,  
                rmin=(current)*tan_low,
                rmax=(current+emcs_pbplate)*tan_upp,
c--
      Call ecal_set_cuts( ag_imed, 'radiator' )
c--
EndBlock
c--
c-----------------------------------------------------------------------------






c----------------------------------------------------------------- Block EFLP --
c--
Block EFLP                 is the aluminum (aluminium) front plate of the endcap
c--
      Material  ALUMINIUM
      Attribute EFLP   seen=1  colo=3  fill=1   lsty=1                   ! GREEN
      Shape     CONS   dz=emcs_front/2,
                rmn1=68.813 rmn2=68.813,
                rmx1=(zslice)*tan_upp+dup,
                rmx2=(zslice+zwidth)*tan_upp+dup,
                phi1=emcs_phimin phi2=emcs_phimax
c--
EndBlock
c-----------------------------------------------------------------------------








c----------------------------------------------------------------- Block EALP --
c--
Block EALP                       is the thin aluminium plate in calorimeter cell
c--
c--
      Material  Aluminium
      Attribute EALP seen=1 colo=1 lsty=1
c--
c--
      Shape     TRD1   dy=emcs_alincell/2  dz=(rtop-rbot)/2
c--
c--   Thin aluminium plate in each calorimeter cell.  The energy-loss
c--   fluctuations are restricted in this thin material.
c--
      CALL GsTPar (AG_IMED,'CUTGAM',0.00001)
      CALL GsTPar (AG_IMED,'CUTELE',0.00001)
      CALL GsTPar (AG_IMED,'LOSS',1.)
      CALL GsTPar (AG_IMED,'STRA',1.)
c--
EndBlock





c----------------------------------------------------------------- Block ESPL --
c--
Block ESPL                         is the logical volume containing an SMD plane
c--
      Material  Air 
      Attribute ESPL   seen=1   colo=4   lsty=4
      Shape     TUBS   dz=emcs_gapsmd/3/2,
                rmin=section*tan_low-1.526,
                rmax=(section-secwid/2)*tan_upp+dup,
                phi1=emcs_phimin phi2=emcs_phimax
c--
      USE EMXG version=1
      msecwd = (emxg_sapex+emxg_f4)/2		
c--   ^^^^^^ what is this used for?  --jw
c--          looks like the g10 layer which we are retiring
c--
c--   loop over the six sectors in an endcap half wheel
c--	
      DO isec=1,6
         cut=1
         d3 = 75 - (isec-1)*30
c--
         IF (exse_sectype(isec)=0|(emcg_fillmode=1&(isec=6|isec=1))) THEN
            cut = 0
c        -- come back and build spacers --
	     ElseIF (exse_sectype(isec) = 1) then !   v
c--
            Create and Position EXSG alphaz=d3 ncopy=isec              kOnly='MANY'
c--
	     ElseIF (exse_sectype(isec) = 2) then               !   u
c--
            Create and Position EXSG alphaz=d3 ort=x-y-z ncopy=isec    kOnly='MANY'
c--
	     ElseIF (exse_sectype(isec) = 3) then               !  cut v
c--
            cut=2
            Create and Position EXSG alphaz=d3 ncopy=isec              kOnly='MANY'
c--
	     ElseIF (exse_sectype(isec) = 4) then               !  cut u 
c--
            cut=2
            Create and Position EXSG alphaz=d3 ort=x-y-z ncopy=isec    kOnly='MANY'
c--
         EndIF
c--
      EndDO! loop over six sectors in eemc half wheel
c--
c--   repeat the loop and add in the spacer layers
c--
      DO isec=1,6
         d3=75 - (isec-1)*30
         IF (exse_sectype(isec)=0|(emcg_fillmode=1&(isec=6|isec=1))) then                                                                               
            cut = 0         
c--                                                                 Do not kill neighbors
            Create and Position EXSG alphaz=d3 ncopy=isec           kOnly='MANY'
c           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
c           potential side effect... may screw up the mapping
c           of the smd strips into the tables?
c
         EndIF
      EndDO
c--
EndBlock











c----------------------------------------------------------------- Block EXSG --
c--
Block EXSG   Is another logical volume... this one acutally creates the planes
c--
c-- Creates:
c-- + EHMS -- shower max strips
c-- + EFLS -- front cover for SMD planes
c-- + EBLS -- back cover for SMD planes
c--
      Attribute EXSG   seen=1   colo=7   serial=cut   lsty=3   ! MEH
      Material  Air
c$$$      Medium    TMED_EXSG stemax=0.01
      Shape     TUBS   dz=emcs_gapsmd/3/2,
                rmin=section*tan_low-1.526,
                rmax=(section-secwid/2)*tan_upp+dup,
                phi1=emcs_phimin/emcs_nsupsec-5,
                phi2=emcs_phimax/emcs_nsupsec+5
c--
      rbot = emxg_rin
      rtop = emxg_rout
c--
c--   Code to handle smd spacers
c--
      IF ( cut .eq. 0 ) THEN
         Create and Position EXPS kONLY='MANY'
      ENDIF
c--
c--   Code to handle smd planes
c--
      IF (cut > 0) THEN
c--
c--     setup which plane we are utilizing
c--
        IF (cut = 1) THEN
           nstr = 288
        ELSE
           nstr = 285
        ENDIF

c--
c--    loop over all smd strips and place them w/in this smd plane
c--
    	DO istrip = 1,nstr
c--
          Call ecal_get_strip( section, cut, istrip, xc, yc, length )
c--
          IF (mod(istrip,2) != 0 ) THEN
             Create and Position EHMS  x=xc y=yc alphaz=-45 kOnly='ONLY'
             Create and Position EBLS  x=xc y=yc z=(+esmd_apex/2+esmd_back_layer/2) alphaz=-45 kOnly='ONLY'
          ELSE
             Create and Position EHMS  x=xc y=yc alphaz=-45 ort=x-y-z kOnly='ONLY'
             Create and Position EFLS  x=xc y=yc z=(-esmd_apex/2-esmd_front_layer/2) alphaz=-45 ort=x-y-z kOnly='ONLY'
          ENDIF
c--
          Prin1 istrip, xc, yc, length
            ( 'SMD Plane: strip=',I3,' xc=',F5.1,' yc=,'F5.1,' length=',F5.1 )
c--
        ENDDO
c--
      ENDIF
c--
c--
*     dcut exsg z 0 0 10 0.1 0.1
*     dcut exsg y 0 10 -50 0.7 0.7
c--
EndBlock
c--
c--
c-----------------------------------------------------------------------------








c----------------------------------------------------------------- Block EHMS --
c--
Block EHMS                                     defines the triangular SMD strips
c--
      Material  Ecal_scint
      Medium    Ecal_active isvol=1
      Attribute EHMS      seen=1    colo=2  serial=cut  lsty=1        ! red
c--
      Shape     TRD1 dx1=0 dx2=emxg_Sbase/2 dy=length/2 dz=emxg_Sapex/2
c--
      HITS EHMS     Birk:0:(0,10)  
c--
Endblock! EHMS
c-----------------------------------------------------------------------------




c---
c-- Several thin layers of material are applied to the front and back of the 
c-- SMD planes to provide structural support.  We combine these layers into
c-- a single effective volume, which is affixed to the base of the SMD
c-- strips.  As with the SMD strips, z along the depth, y is length
c--
c-- http://drupal.star.bnl.gov/STAR/system/files/SMD_module_stack.pdf
c--
c-- 1.19 mm G10
c-- 0.25 mm Fiberglass and epoxy
c-- 0.17 mm Aluminized mylar
c--
c-- Weight in mixture by mass = (depth)*(Area)
c--
c-- Weighted density is given by sum (density)_i * (depth)_i / sum (depth)_i
c--


c----------------------------------------------------------------- Block EFLS --
c--
Block EFLS               is the layer of material on the front of the SMD planes
c--
c--
      Component G10        A=18.017 Z=9.013 w=1.19*1.700/(1.19*1.700+0.25*1.530+0.17*1.390)
      Component Fiberglass A=19.103 Z=9.549 w=0.25*1.530/(1.19*1.700+0.25*1.530+0.17*1.390) 
      Component AlMylar    A=12.889 Z=6.465 w=0.17*1.390/(1.19*1.700+0.25*1.530+0.17*1.390) 
      Mixture   EFLS       dens=(1.19*1.7+0.25*1.53+0.17*1.39)/(1.19+0.25+0.17)

      Attribute EFLS seen=1 colo=22 lsty=1
      Shape     TRD1 dz=esmd_front_layer/2 dy=length/2 dx1=esmd_base/2 dx2=esmd_base/2 
c--
EndBlock! EFLS


c--
c-- see link above for documentation
c--
c-- 0.10 mm aluminized mylar
c-- 0.25 mm fiberglass and epoxy
c-- 1.50 mm WLS fiber router layer (polystyrene)
c-- 0.25 mm aluminum
c--


c----------------------------------------------------------------- Block EBLS --
c--
Block EBLS                is the layer of material on the back of the SMD planes
c--
      Component AlMylar    A=12.889 Z=6.465   w=0.10*1.390/(0.10*1.390+0.25*1.530+1.50*1.032+0.25*2.699)  
      Component Fiberglass A=19.103 Z=9.549   w=0.25*1.530/(0.10*1.390+0.25*1.530+1.50*1.032+0.25*2.699)  
      Component Polystyren A=11.154 Z=5.615   w=1.50*1.032/(0.10*1.390+0.25*1.530+1.50*1.032+0.25*2.699)  
      Component Al         A=28.08  Z=14.00   w=0.25*2.699/(0.10*1.390+0.25*1.530+1.50*1.032+0.25*2.699)  
      Mixture   EBLS       dens=(0.10*1.390+0.25*1.530+1.50*1.032+0.25*2.699)/(0.10+0.25+1.50+0.25)
c--
      Attribute EFLS seen=1 colo=22 lsty=1
      Shape     TRD1 dz=esmd_back_layer/2 dy=length/2 dx1=esmd_base/2 dx2=esmd_base/2 
c--
EndBlock! EFLS






c----------------------------------------------------------------- Block EXPS --
c--
Block EXPS                   is the plastic spacer in the shower maximum section
c--
c--   Simple implementation of the spacer in the shwoer maximum detector.
c--   This implmentation neglects the ears and the source tube.
c--
c--      n.b.  There may be a side effect in the way this gets created...
c--            it could overwrite SMD strips which extend into this plane.
c--            Probably need to go with a different approach here.
c--
c--   Scanned Drawings:
c--   + http://drupal.star.bnl.gov/STAR/system/files/SMD_spacer_drawings.pdf
c--
c--     thickness is 1.2 cm, as given by detail B and C... but I do not want
c--     to do alot of complicated recoding of the geometry.  So I am limiting
c--     it to be the same width as a normal SMD volume.
c--
      Material  PVC_Spacer
      Attribute EXPS   seen=1   colo=6    lsty=1    lwid=2
c--
c--   Spacer layers are extended by +/- 5 degrees into the adjacent sectors.
c--   The kONLY='Many' option at creation time should mean that conflicts
c--   in volume will be resolved in favor of the SMD strips.
c--
      Shape   TUBS   dz=esmd_apex/2,
              rmin=(section)*Tan_Low-1.526,
              rmax=(section+msecwd)*Tan_Upp,
              phi1=emcs_PhiMin/emcs_Nsupsec,
              phi2=emcs_PhiMax/emcs_Nsupsec
c--
EndBlock
c--
END
c----------------------------------------------------------------- End Module --












c------------------------------------------------------------------------------
c--                                           Helper subroutines and functions

c------------------------------------------------------------------------------
c--
c-- Subroutine ecal_set_cuts(id, medium)
c--
c--   id -- integer ID idetifying the current tracking medium
c--   medium -- character switch selecting the type of cuts to be
c--             used in this tracking volumne
c--
c------------------------------------------------------------------------------
        Subroutine ecal_set_cuts(id,medium)         
c--
          Implicit NONE
          Integer    id
          Character  medium*(*)
c--
          Integer radiator, megatile, detector
          Save    radiator, megatile, detector
c--
          IF ( medium == 'print' ) THEN
c--
            Write (*,400) radiator
            Write (*,401) megatile
            Write (*,402) detector
c--
            Call GpTMed( +radiator )
            Call GpTMed( -megatile )
            Call GpTMed( -detector )
c--
            Return
c--
          ENDIF
c--
  400     Format('radiator cuts set for ag_imed=',I3)
  401     Format('megatile cuts set for ag_imed=',I3)
  402     Format('detector cuts set for ag_imed=',I3)
c--

c--
c--       Setup common cuts for neutrons, hadrons and muons
c--
          Call GsTPar (id,'CUTNEU',0.001)
          Call GsTPar (id,'CUTHAD',0.001)
          Call GsTPar (id,'CUTMUO',0.001)
c--
          IF ( medium == 'radiator' ) THEN
               Call GsTPar (id,'CUTGAM',0.00008)
               Call GsTPar (id,'CUTELE',0.001)
               Call GsTPar (id,'BCUTE' ,0.0001)
               radiator = id
C--       
c--
          ELSEIF ( medium == 'megatile' ) THEN
               Call GsTPar (id,'CUTGAM',0.00008)
               Call GsTPar (id,'CUTELE',0.001)
               Call GsTPar (id,'BCUTE' ,0.0001)
               megatile = id
c--
c--
          ELSEIF ( medium == 'detector' ) THEN
               Call GsTPar (id,'CUTGAM',0.00008)
               Call GsTPar (id,'CUTELE',0.001)
               Call GsTPar (id,'BCUTE' ,0.0001)
c--
               Call GsTPar (id,'BIRK1',1.)
               Call GsTPar (id,'BIRK2',0.0130)
               Call GsTPar (id,'BIRK3',9.6E-6)
               detector = id
c--
c--
           ELSE
               Call GsTPar (id,'CUTGAM',0.00008)
               Call GsTPar (id,'CUTELE',0.001)
               Call GsTPar (id,'BCUTE' ,0.0001)
               Write(*,300) 
  300          Format('Warning: unknown medium[',A20,'] in ecal_set_cuts')
c--
c--
          ENDIF
c--
          Return
         
        End
c-----------------------------------------------------------------------
c-----------------------------------------------------------------------
c--
c--
        Subroutine ecal_get_strip( section, cut, istrip, xcenter, ycenter, length )
c--                                in       in   in      out      out      out
          Implicit NONE
c--
          Real     section
          Integer  cut         ! 0=no plane  1=normal plane  2=cut plane
          Integer  istrip      ! strip index
          Real     xcenter     ! output
          Real     ycenter     ! output
          Real     length      ! output
c--
          Integer  nstrips     
          Real     rdel        ! shift in radius (?)
          Real     rth
          Real     ddn, ddup   
          Real     megatile, p
c--
          Real     xleft, yleft, xright, yright 
          Real     dxy, xx, yy
          Real     sqrt2, sqrt3
c--
c--       SMD data copied from data structures above
c--
          Real base, apex
          Data base, apex / 1.0, 0.7/ !cm
c--
          Real Rbot, Rtop
          Data Rbot, Rtop / 77.41, 213.922 /
c--
          Real EtaMin, EtaMax
          Data EtaMin, EtaMax / 1.086, 2.000 /
c--
          Real tan_theta_min, tan_theta_max
c--
          Real tanf, eta
          tanf(eta) = tan(2*atan(exp(-eta)))
c--
          tan_theta_min = tanf( EtaMax )
          tan_theta_max = tanf( EtaMin )
c--
          IF (cut    = 1) THEN                                                                                                       
             rdel    = 3.938                                                                                                         
             nstrips = 288                                                                                                           
          ELSE                                                                                                                    
             rdel    = -.475                                                                                                         
             nstrips = 285                                                                                                           
          ENDIF               
c--
          xcenter=0. 
          ycenter=0.
          length=0.
c--
          IF ( cut = 0 ) THEN
          RETURN
          ENDIF
c--
          sqrt2 = sqrt(2.0)
          sqrt3 = sqrt(3.0)
c--
          rth = .53*rdel        ! .53 --- tentatavily    jcw-- wtf?                                                               
          ddn = sqrt(3.0)*1.713 + rdel                                                                                                  
          ddup = .5*1.846 + 1.713             
          megatile = base + .01
c--
          p = .5*(istrip-1)*megatile + 41.3655  

          IF (p <= (.5*rbot*sqrt3 + rth)) THEN
          dxy     = 1.9375*sqrt2
          xleft  = .5*sqrt2*p*(sqrt3 + 1.) - dxy
          yleft  = .5*sqrt2*p*(sqrt3 - 1.) - dxy 
          yright = .5*sqrt2*(sqrt( rbot*rbot - p*p) - p)
          xright = sqrt2*p + yright
          ELSEIF ((.5*rbot*sqrt3  + rth) < p <= (.5*rtop + 1.5)) THEN
          dxy = 1.9375*sqrt2
          xleft = .5*sqrt2*p*(sqrt3 + 1.) - dxy
          yleft = .5*sqrt2*p*(sqrt3 - 1.) - dxy 
          dxy = rdel*sqrt2/sqrt3
          yright = .5*sqrt2*p*(1.- 1./sqrt3)
          xright = sqrt2*p - yright - dxy
          yright = -yright - dxy
          ELSEIF (p > (.5*rtop +1.5)) THEN
          yleft = (sqrt(rtop*rtop - p*p) - p)/sqrt2
          xleft = sqrt2*p + yleft
          dxy = rdel*sqrt2/sqrt3
          yright = .5*sqrt2*p*(1.- 1./sqrt3)
          xright = sqrt2*p - yright - dxy
          yright = -yright - dxy
          dxy = 0. 
c--
          IF ((.5*sqrt3*160.- ddn) < p <= (.5*sqrt3*160.+ ddup) ) THEN
          xcenter = .5*(sqrt3*160.+1.846)
          ycenter = xcenter - .5*sqrt3*1.713
          IF (p > ycenter) THEN
              dxy = .5*sqrt2*(2/sqrt3*rdel + .5*sqrt3*1.846 +_
              sqrt(1.713*1.713 - (p-xcenter)*(p-xcenter)))
          ELSE
              dxy = sqrt2/sqrt3*(p - .5*sqrt3* 160. + ddn)
          ENDIF
          ELSEIF ((.5*sqrt3*195.- ddn) < p <= (.5*sqrt3*195. + ddup) ) THEN
          xcenter = .5*(sqrt3*195.+1.846)
          ycenter = xcenter - .5*sqrt3*1.713
          IF (p > ycenter) THEN
             dxy = .5*sqrt2*(2/sqrt3*rdel + .5*sqrt3*1.846 +_
             sqrt(1.713*1.713 - (p-xcenter)*(p-xcenter)))
          ELSE
             dxy = sqrt2/sqrt3*(p - .5*sqrt3*195. + ddn)
          ENDIF
          ENDIF
             xright = xright + dxy
             yright = yright + dxy
          ENDIF

          dxy     =  section*tan_theta_max - rtop                                                                                                            
          xcenter = .5*(xright+xleft) + dxy                                                                                                            
          ycenter = .5*(yright+yleft)                                                                                                                  
          xx = .5*sqrt2*(xleft+yleft)                                                                                                               
          yy = .5*sqrt2*(xright+yright)                                                                                                             
          length = xx-yy                              
c--
c--          
          Return
c--
        End! Subroutine smd_strip
c--
* ----------------------------------------------------------------------------
* ECAL nice views: dcut ecvo x 1       10 -5  .5 .1
*                  draw emdi 105 0 160  2 13  .2 .1
*                  draw emdi 120 180 150  1 14  .12 .12
* ---------------------------------------------------------------------------



c-- examples of HITS
*      HITS EHMS     Birk:0:(0,10)  
*                     xx:16:SH(-250,250)  yy:16:(-250,250)  zz:16:(-350,350),
*                     px:16:(-100,100)    py:16:(-100,100)  pz:16:(-100,100),
*                     Slen:16:(0,1.e4)    Tof:16:(0,1.e-6)  Step:16:(0,100),
*                     none:16:            Eloss:0:(0,10)
*

2009.10.12 Jason EEMC geometry: effects of adding layers

Effect of added layers in Jason geometry file (ecalgeo.g23)

Monte-Carlo setup:

One photon per event
EEMC only geometry with LOW_EM option
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and pt (6-10 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy
(no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Cuts for shower shapes:
Single particle kinematic cuts: pt=7-8GeV, eta=1.2-1.4
All shapes are normalized to 1 at peak (central strip)

Added layer definition from Jason file:

EXPS is the plastic spacer in the shower maximum section
EBLS is the layer of material on the back of the SMD planes
EFLS is the layer of material on the front of the SMD planes

Some comments:

Figs. 1-2 show that I can reproduce
sampling fraction and shower shapes
which I see with geometry file from CVS
if I disable all three added layers in Jason geometry file
(this assumes/shows that G10 layer have tiny effect).
This a good starting point, since it indicate that
all other (cosmetic) code modifications
are most probably done correctly and has no
effect on simulated detector response.
Fig. 3 shows effect of each added layer
(plastic spacers and layers in front/back of SMD)
on the sampling fraction and 2x1/3x3 energy profile:
- Each layer contributes more or less equally to the sampling fraction.
- Energy profile (E2x1 / E3x3) does not affected by the added layers
Fig. 4 shows effect of each added layer on the shower shapes:
- Back SMD layer does not contribute much (as expected).
- Front and spacers introduce equal amount of "shape narrowing".
Figs. 5-6 show pre-shower sorted shower shapes
and comparison with eta-meson shapes.

No layers and G10 removed

Figure 1: Sampling fraction vs. thrown energy

Figure 2: Shower shapes

Adding new laters (spacer, front, back)

Figure 3: Sampling fraction vs. thrown energy (left), 2x1/3x3 energy ratio (right)
See legend for details

Figure 4: Shower shapes. See legend for details

Shower shapes sorted by pre-shower energy

Pre-shower bins:

Ep1 = 0, Ep2 = 0 (no energy in both EEMC pre-shower layers)
Ep1 = 0, Ep2 > 0
0 < Ep1 < 4 MeV
4 < Ep1 < 10 MeV
Ep1 > 10 MeV
All pre-shower bins combined

Figure 5: Shower shapes (left) and their ratio (right)

Figure 6: Shower shape ratios

2009.10.13 Jason EEMC geometry: position correlations

Effect of added layers in Jason geometry file (ecalgeo.g23)

Monte-Carlo setup:

One photon per event
EEMC only geometry with LOW_EM option
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and pt (6-10 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy
(no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Added layer definition from Jason file:

EXPS is the plastic spacer in the shower maximum section
EBLS is the layer of material on the back (routing layers) of the SMD planes
EFLS is the layer of material on the front (G10, etc) of the SMD planes

Geometry configurations and notations (shown in the center of the plot):

j-noLayers: Jason geometry: no EXPS, EBLS, EFLS
j-back: Jason geometry, EBLS only
j-front: Jason geometry, EFLS only
j-spacer: Jason geometry, EXPS only
j-all: Jason geometry, all new layers included
geom-cvs geometry file from CVS after cAir bug fixed

cross section of 1st SMD plane labeled with "SUV" ordering

Note: u-v ordering scheme can be found here (Fig. 9-11)

Figure 1: Average number of SMD u-strip fired vs. thrown photon's (x,y)

Figure 2:Average number of SMD v-strip fired vs. thrown photon's (x,y)

Figure 3:Average SMD u-energy vs. thrown photon's (x,y)

Figure 4:Average SMD v-energy vs. thrown photon's (x,y)

2009.10.16 Jason geometry file: Full STAR simulations

Monte-Carlo setup:

One photon per event
EEMC only and Full STAR geometry configurations with LOW_EM option
(using Victor's geometry fix)
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and pt (6-10 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy
(no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Geometry configurations and notations (shown in the center of the plot):

eemc-cvs: EEMC only with geometry file from CVS (cAir-fixed)
full-cvs: Full STAR with geometry file from CVS (cAir-fixed)
eemc-j: EEMC only with Jason geometry file
full-j: Full STAR with Jason geometry file

Figure 1: Sampling fraction

Figure 2: Total energy distribution

Figure 3: Shower shapes (left) and shape ratios (right) for 0 < pre-shower1 < 4MeV

Pre-shower sorted shapes (for completeness)

Figure 4: Shower shapes (all pre-shower bins)

Figure 5: Shower shapes ratio (all pre-shower bins)

2009.10.20 Sampling fraction problem: full STAr vs. EEMC stand alone geometry

For the previous study click here

Monte-Carlo setup:

One photon per event
EEMC only and Full STAR geometry configurations with LOW_EM option
(using Victor's geometry fix)
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and pt (6-10 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy
(no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Geometry configurations and notations (shown in the center of the plot):

eemc-cvs: EEMC only with geometry file from CVS (cAir-fixed)
full-cvs: Full STAR with geometry file from CVS (cAir-fixed)
eemc-j: EEMC only with Jason geometry file
full-j: Full STAR with Jason geometry file

Figure 1: Average energy in SMD-u plane vs. position of the thrown photon

SMD v (left) and u (right) sampling fraction (E_smd/E_thrown) vs. E_thrown

Figure 2: Sampling fraction (E_tower^total/E_thrown) vs. position of the thrown photon

Sampling fraction (E_tower^total/E_thrown) vs. E_thrown

Figure 3: Number of towers above threshold vs. position of the thrown photon

Number of towers above threshold vs. E_thrown

Other EEMC layers: pre-shower, postshower

Figure 4: (left) Pre-shower1 and (right) Pre-shower2 sampling fraction vs. E_thrown

Figure 5: (left) High tower sampling fraction and (right) residual energy, [E_tot-E_3x3]/E_thrown, vs. E_thrown

2009.10.26 Jason vs. CVS EEMC: removed SMD layers

Monte-Carlo setup:

One photon per event
Disabled new SMD layers (EXPS EBLS EFLS) in Jason geometry
EEMC only and Full STAR geometry configurations with LOW_EM option
Note: LOW_EM option seems not to work for EEMC only configuration (double checking)
(using Victor's geometry fix)
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and pt (6-10 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy
(no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Geometry configurations and notations (shown in the center of the plot):

eemc-cvs: EEMC only with geometry file from CVS (cAir-fixed)
full-cvs: Full STAR with geometry file from CVS (cAir-fixed)
eemc-j-noL: EEMC only with Jason geometry file (disabled 3-new SMD layers)
full-j-noL: Full STAR with Jason geometry file (disabled 3-new SMD layers)

Figure 1: number of post-shower tiles

Figure 2: number of pre-1-shower tiles

Figure 3: number of pre-2-shower tiles

Figure 4: number of towers

2D

Figure 5: Average pre-shower1 energy

Figure 6: Average pre-shower2 energy

Figure 7: Average number of SMD-u strips

Figure 8: Average number of SMD-v strips

Figure 9: Average post-shower energy

Sampling fraction

Figure 10: Sampling fraction 1x1 vs. thrown energy

Figure 11: Sampling fraction 2x1 vs. thrown energy

Figure 12: Sampling fraction 3x3 vs. thrown energy

Figure 13: Sampling fraction (total energy) vs. thrown energy

Figure 14: Sampling fraction 1x1

Figure 15: Sampling fraction 2x1

Figure 16: Sampling fraction 3x3

SMD shower shapes

Figure 17: SMD shower shape (v-plane)

2009.10.27 Jason EEMC geometry: effect of removing new SMD layers

Monte-Carlo setup:

One photon per event
Disabled/Enabled new SMD layers (EXPS EBLS EFLS) in Jason geometry
EEMC only and Full STAR geometry configurations with LOW_EM option
(using Victor's geometry fix)
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and pt (6-10 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy
(no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Geometry configurations and notations (shown in the center of the plot):

eemc-j: EEMC only with Jason geometry file
full-j: Full STAR with Jason geometry file
eemc-j-noL: EEMC only with Jason geometry file (disabled 3-new SMD layers)
full-j-noL: Full STAR with Jason geometry file (disabled 3-new SMD layers)

Effect of removing SMD layers on SMD strips

Figure 1: Average number of SMD-u strips

Figure 2: Average number of SMD-v strips

Effect of removing SMD layers on sampling fraction

Figure 3: distribution of 1x1 sampling fraction

Figure 4: distribution of 2x1 sampling fraction

Figure 5: distribution of 3x3 sampling fraction

Figure 6: 1x1 sampling fraction vs. thrown energy

Figure 7: 2x1 sampling fraction vs. thrown energy

Figure 8: 3x3 sampling fraction vs. thrown energy

2009.10.27: Jason EEMC geometry: comparison without LOW_EM option

Monte-Carlo setup:

One photon per event
Disabled new SMD layers (EXPS EBLS EFLS) in Jason geometry
EEMC only and Full STAR geometry configurations without LOW_EM option
(using Victor's geometry fix)
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and pt (6-10 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy
(no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Geometry configurations and notations (shown in the center of the plot):

eemc-cvs: EEMC only with geometry file from CVS (cAir-fixed)
full-cvs: Full STAR with geometry file from CVS (cAir-fixed)
eemc-j-noL: EEMC only with Jason geometry file (disabled 3-new SMD layers)
full-j-noL: Full STAR with Jason geometry file (disabled 3-new SMD layers)

Figure 1: Sampling fraction 1x1

Figure 2: Sampling fraction 2x1

Figure 3: Sampling fraction 3x3

Figure 4: Sampling fraction total energy

Figure 5: Sampling fraction pre1-shower

Figure 6: Sampling fraction pre2-shower

Figure 7: Sampling fraction smd-u

Figure 8: Sampling fraction smd-v

Figure 9: Sampling fraction post-shower

Sampling fraction vs. thrown energy

Figure 10: Sampling fraction 1x1 vs. thrown energy

Figure 11: Sampling fraction 2x1 vs. thrown energy

Figure 12: Sampling fraction 3x3 vs. thrown energy

Figure 13: Sampling fraction (tatal energy) vs. thrown energy

2009.10.30: Jason EEMC geometry: Jason with ELED block from CVS file

FYI: Alice blog on ELED block study

Monte-Carlo setup:

One photon per event
Disabled SMD layers (EXPS EBLS EFLS) in Jason geometry
Put ELED block from CVS file into Jason geometry
geometry configurations without LOW_EM option
(using Victor's geometry fix)
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and pt (6-10 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy
(no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Geometry configurations and notations (shown in the center of the plot):

full-cvs: Full STAR with geometry file from CVS (cAir-fixed)
full-j: EEMC only with Jason geometry file (disabled 3-new SMD layers, ELED block replaced with that from CVS)

Figure 1: Sampling fraction 1x1 (up-left), 2x1 (up-right), 3x3 (low-left), total energy (low-right)

Figure 2: Sampling fraction pre1 (up-left), pre2 (up-right), SMD-u (low-left), post (low-right)

Figure 3: Shower shapes (left) and shower shape ratio (right)

11 Nov

November 2009 posts

2009.11.02 Jason EEMC geometry: results with and without LOW_EM options

Monte-Carlo setup:

One photon per event
Disabled SMD layers (EXPS EBLS EFLS) in Jason geometry
geometry configurations with and without LOW_EM option
(using Victor's geometry fix)
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and pt (6-10 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy
(no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Geometry configurations and notations (shown in the center of the plot):

full-cvs-noEM (dashed): CVS geometry (cAir-fixed) without LOW_EM option
full-cvs-EM (solid): CVS geometry (cAir-fixed) with LOW_EM option
full-j-NoEM-noL: Jason geometry (disabled 3-new SMD layers) without LOW_EM option
full-j-EM-noL: Jason geometry (disabled 3-new SMD layers) with LOW_EM option

Figure 1: Distribution of the sampling fraction (total energy in EEMC)

Figure 2: Sampling fraction (total energy in EEMC) vs. thrown energy

Figure 3: Sampling fraction (total energy in EEMC) vs. position of the thrown photon

2009.11.03 BEMC sampling fraction: with and without LOW_EM option

Monte-Carlo setup:

Throwing one photon per event
Full STAR geometry (y2006g) configurations with and without LOW_EM option.
Note: LOW_EM cuts are listed at the bottom of this page,
and some related discussion can be found in this phana thread
Throw particles flat in eta (-1,1), phi (0, 2pi), and energy (30 +/- 0.5 GeV)
Vertex z=0
50K/per particle type

Geometry configurations and notations:

BEMC-noLOW_EM: Full STAR y2006g without LOW_EM option
BEMC-LOW_EM: Full STAR y2006g with LOW_EM option

data base settings (same settings in bfc.C (Jan's trick) and in my MuDst reader):
dbMk->SetFlavor("sim","bemcPed");
dbMk->SetFlavor("Wbose","bemcCalib");
dbMk->SetFlavor("sim","bemcGain");
dbMk->SetFlavor("sim","bemcStatus");

dbMk->SetFlavor("sim","bprsPed");
dbMk->SetFlavor("Wbose","bprsCalib");
dbMk->SetFlavor("sim","bprsGain");
dbMk->SetFlavor("sim","bprsStatus");

dbMk->SetFlavor("sim","bsmdePed");
dbMk->SetFlavor("Wbose","bsmdeCalib");
dbMk->SetFlavor("sim","bsmdeGain");
dbMk->SetFlavor("sim","bsmdeStatus");

dbMk->SetFlavor("sim","bsmdpPed");
dbMk->SetFlavor("Wbose","bsmdpCalib");
dbMk->SetFlavor("sim","bsmdpGain");
dbMk->SetFlavor("sim","bsmdpStatus");

Note: for BEMC ideal pedSigma set to 0, so effectively
there is no effect when I apply 3-sigma threshold above pedestal.

Figure 1: E_reco/E_thrown distribution.
E_reco is the total energy in the BEMC towers from mMuDstMaker->muDst()->muEmcCollection()
E_thrown energy of the thrown photon from tne GEant record
No cut (yet) applied to exclude otliers in the average
Outliers in E_reco/E_thrown

Figure 2: Average E_reco/E_thrown vs. thrown photon eta (left) and phi (right)
Average is taken over a slice in eta or phi (no gaussian fits)

Figure 3: Average E_reco/E_thrown vs. thrown position (eta and phi)
Left: without LOW_EM option; right: with LOW_EM option
No cut applied to exclude otliers

2009.11.03 Jason EEMC geometry: Effect of ELED block change

Monte-Carlo setup:

One photon per event
Disabled SMD layers (EXPS EBLS EFLS) in Jason geometry
Alter the ELED block (lead absorber plate) in Jason geometry file
Full STAR geometry configurations with and without LOW_EM option
(using Victor's geometry fix)
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and pt (6-10 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy
(no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Figure 1: Sampling fraction (total energy in EEMC)

Solid symbols and lines present results with LOW_EM option
Note: the black are the same in left and right plots
Open/dashed symbols and lines - results without LOW_EM option
Upper plots - distribution of the sampling fcation
Lower plots - Sampling fcation vs. thrown photon energy

Left plots: CVS geometry vs. Jason with removed extra SMD layers.
ELED block is the same in all 4 cases, and is taken from CVS file.
in red: CVS geometry, in black - Jason geometry
Right plots:
Jason with new ELED block (in red) vs. Jason with ELED block from CVS (in black)
Extra SMD layers are removed in all 4 cases

Figure 2: Sampling fraction (total energy in EEMC)
black: same black as in Fig. 1, upper plots
red: EEMC geometry with Material PbAlloy isvol=0
(modification suggested by Jason in this post)

2009.11.06 new EEMC geometry: Pure lead and new SMD layers

Monte-Carlo setup:

One photon per event
Disabled/Enabled SMD layers (EXPS EBLS EFLS) in Jason geometry
Alter the ELED block with pure lead
Full STAR geometry configurations with and without LOW_EM option
(using Victor's geometry fix)
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and pt (6-10 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy
(no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Geometry configurations

dashed/open red (j-noEM,noL,Pb):
full STAR y2006, no LOW_EM, Jason EEMC geometry without new SMD layers, pure lead in ELED block
solid red (j-EM,noL,Pb):
full STAR y2006, LOW_EM, Jason EEMC geometry without new SMD layers, pure lead in ELED block
dashed/open black (j-noEM,Pb):
full STAR y2006, no LOW_EM, Jason EEMC geometry with new SMD layers, pure lead in ELED block
solid black (j-EM,Pb):
full STAR y2006, LOW_EM, Jason EEMC geometry with new SMD layers, pure lead in ELED block

Sampling fraction of various EEMC layers (tower, SMD, pre1-,pre2-, post- shower)

Figure 1: Tower sampling fraction distribution

Figure 2: Tower sampling fraction vs. thrown energy

Figure 3: Tower sampling fraction vs. position of the thrown photon

Figure 4: Pre1, pre2, post and SMD sampling fraction distribution

Figure 5: Pre1, pre2, post and SMD sampling fraction vs. thrown energy

SMD shower shapes

Figure 6: SMD-v shower shapes

Figure 7: SMD-v shower shape ratios

Figure 8: Number of SMD-u strips

Figure 9: Number of SMD-v strips

Tower energy profile

Figure 10: Energy ractio of 2x1 to 3x3 cluster vs. gamma-jet data

Energy deposition in various EEMC layers vs. position of the thrown photon

Figure 11: Pre-shower1 energy

Figure 12: Pre-shower2 energy

Figure 13: Post-shower energy

Figure 14: SMD-v energy

Figure 15: Number of towers

LOW_EM option and pre-shower migration

Figure 16: Tower Sampling fraction: LOW_EM option and pre-shower migration

2009.11.10 BEMC sampling fraction and clustering

Monte-Carlo setup:

Throwing one photon per event
Full y2009 STAR geometry configurations with and without LOW_EM option.
Note: LOW_EM cuts are listed at the bottom of this page,
and some related discussion can be found in this phana thread
Throw particles flat in eta (-0.95,0.05) amd (0.05, 0.95), phi (0, 2pi), and energy (30 +/- 0.5 GeV)
bfc.C options:
trs,fss,y2009,Idst,IAna,l0,tpcI,fcf,ftpc,Tree,logger,ITTF,Sti,MakeEvent,McEvent,
geant,evout,IdTruth,tags,bbcSim,tofsim,emcY2,EEfs,
GeantOut,big,-dstout,fzin,-MiniMcMk,beamLine,clearmem,eemcDB,VFPPVnoCTB
Use fixed (7%) sampling fraction in StEmcSimpleSimulator.cxx
mSF[0] = 1/0.07;
mSF[1] = 0.;
mSF[2] = 0.;
Vertex z=0
50K/per particle type

Geometry configurations and notations:

BEMC-noLOW_EM: Full STAR y2009 without LOW_EM option
BEMC-LOW_EM: Full STAR y2009 with LOW_EM option

Note: for BEMC ideal pedSigma set to 0, so effectively
there is no effect when I apply 3-sigma threshold above pedestal.

Figure 1: Sampling fraction (0.07*E_reco/E_thrown) distribution: average vs. gaussian fit
E_reco is the total energy in the BEMC towers from mMuDstMaker->muDst()->muEmcCollection()
E_thrown energy of the thrown photon from tne GEant record
The difference between fit and using average values is < 0.7%

Figure 2: Otliers vs. eta and phi: (left) no energy reconstrycted, (right) s.f. < 55%
Most outlier are at eta = 0, -1, +1

Figure 3: Sampling fraction (0.07*E_reco/E_thrown) distribution
Effect of LOW_EM cuts

Figure 4: Sampling fraction vs. thrown photon eta (left) and phi (right)
Average is taken over a slice in eta or phi with cut on outliers (events with s.f. < 5.5% rejected)

Figure 5: Sampling fraction vs. thrown position (eta and phi)
Average is taken over a slice in eta or phi with cut on outliers (events with s.f. < 5.5% rejected)

Figure 6: (left) Single tower sampling fraction
and (right) energy ratio of 1x1 cluster to the total BEMC energy
Not much of the effect from LOW_EM cuts on the 1x1 clustering. Need to look at other (2x1, 2x2 clusters)

2009.11.11 Tests of EEMC geometry, version 6.1

Monte-Carlo setup:

Throwing one photon per event
Compare EEMC geometry v6.0 (pure lead) vs. v6.1
Full STAR geometry configurations with and without LOW_EM option
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and pt (6-10 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

FYI: tests with v6.1 by Alice

Geometry configurations

dashed/open red: full STAR y2006, no LOW_EM, EEMC geometry v6.1
solid red: full STAR y2006, with LOW_EM, EEMC geometry v6.1
dashed/open black: full STAR y2006, full STAR y2006, no LOW_EM, EEMC geometry v6.0
solid black: full STAR y2006, full STAR y2006, with LOW_EM, EEMC geometry v6.0

Sampling fraction of various EEMC layers (tower, SMD, pre1-,pre2-, post- shower)

Figure 1: Sampling fraction of various EEMC layers vs. thrown photon energy:
(a) tower s.f.; (b) tower s.f. distribution; (c) pre-shower1; (d) pre-shower2; (e) SMD, (f) post-shower

Figure 2: (left) Shower shapes and (right) shower shape ratios

2009.11.16 Tests of EEMC geometry, version 6.1: lead vs. mixture

Monte-Carlo setup:

Throwing one photon per event
Compare EEMC geometry v6.1 with pure lead vs. mixture
Full STAR geometry configurations with and without LOW_EM option
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and pt (6-10 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Geometry configurations

dashed/open red: full STAR y2006, no LOW_EM, EEMC geometry v6.1 with pure lead
solid red: full STAR y2006, with LOW_EM, EEMC geometry v6.1 with pure lead
dashed/open black: full STAR y2006, full STAR y2006, no LOW_EM, EEMC geometry v6.1 with lead-ally mixture
solid black: full STAR y2006, full STAR y2006, with LOW_EM, EEMC geometry v6.1 with lead-ally mixture

Figure 1: EEMC sampling fraction vs. thrown photon energy:

2009.11.17 BEMC sampling fraction: energy dependence

Monte-Carlo setup:

Throwing one photon per event
Full y2009 STAR geometry configurations with LOW_EM option
Throw particles flat in eta (-1,1), phi (0, 2pi),
with energy steps: 10, 20, 30, 40, and 50 GeV with flat (+/-0.5 GeV) spread
bfc.C options:
trs,fss,y2009,Idst,IAna,l0,tpcI,fcf,ftpc,Tree,logger,ITTF,Sti,MakeEvent,McEvent,
geant,evout,IdTruth,tags,bbcSim,tofsim,emcY2,EEfs,
GeantOut,big,-dstout,fzin,-MiniMcMk,beamLine,clearmem,eemcDB,VFPPVnoCTB
Use fixed (7%) sampling fraction in StEmcSimpleSimulator.cxx
mSF[0] = 1/0.07;
mSF[1] = 0.;
mSF[2] = 0.;
Vertex z=0
50K/per particle type

Note: for BEMC ideal pedSigma set to 0, so effectively
there is no effect when I apply 3-sigma threshold above pedestal.

Figure 1: Rapidity cuts study (no eta cuts, no cuts on otliers in this figure)

Figure 2: Sampling fraction (0.07*E_reco/E_thrown) distribution
E_reco is the total energy in the BEMC towers from mMuDstMaker->muDst()->muEmcCollection()
E_thrown energy of the thrown photon from tne Geant record
Cuts: |eta| < 0.97 && |eta|>0.01 && s.f. > 0.055
s.f. distribution on the log scale

2009.11.19 LOW_EM and EEMC time/event in starsim

Monte-Carlo setup:

Throwing one photon/electron per event
y2009 geometry tag (EEMC geometry v6.1)
Full STAR geometry configurations with and without LOW_EM option
Throwing particles flat in eta (1.08, 2.0), phi (0, 2pi), and energy (5-35 GeV)
~50K/per particle type, 250 events per job, 200 jobs

Geometry configurations

red: without LOW_EM option
black: with LOW_EM option
circles - electrons, squares - photons

Figure 1: (left) time/event distribution, (right) average time for the particle type

Conclusion: for single particle Monte-Carlo required time in starsim
with LOW_EM option is ~ 2.6 times higher.

2009.11.23 New EEMC geometry (CVS v6.1): y2006 vs. y2009 STAR configurations

Monte-Carlo setup:

Throwing one photon per event
Compare new EEMC geometry in CVS for y2006 and 2009 configurations
Full STAR geometry configurations with and without LOW_EM option
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and energy (5-35 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Geometry configurations

red: full STAR y2009, with/without LOW_EM, EEMC geometry
black: full STAR y2006, with/without LOW_EM, EEMC geometry v6.1

Figure 1: EEMC sampling fraction (left) distribution (right) vs. thrown photon energy (1.2 < eta < 1.9; no pt cuts)

Figure 2: EEMC sampling fraction (left) distribution (right) vs. thrown photon energy (1.2 < eta < 1.9; pt > 7GeV cut)

Figure 3: 2x1/3x3 clustering

Figure 4: Shower shapes

Figure 5: Shower shape ratios (v plane)

Figure 6: Shower shape ratios (u plane)

Figure 7: Pre-shower migration (1.2 < eta < 1.9; no pt cuts)

12 Dec

December 2009 posts

2009.12.01 BEMC 1x1, 2x1, 2x2, 3x3 clustering

Monte-Carlo setup:

Throwing one photon per event
Full y2009 STAR geometry configurations with/without LOW_EM option
Throw particles flat in eta (-1,1), phi (0, 2pi),
with energy: 30GeV with flat (+/-0.5 GeV) spread
bfc.C options:
trs,fss,y2009a,Idst,IAna,l0,tpcI,fcf,ftpc,Tree,logger,ITTF,Sti,MakeEvent,McEvent,
geant,evout,IdTruth,tags,bbcSim,tofsim,emcY2,EEfs,
GeantOut,big,-dstout,fzin,-MiniMcMk,beamLine,clearmem,eemcDB,VFPPVnoCTB
Use fixed (7%) sampling fraction in StEmcSimpleSimulator.cxx
mSF[0] = 1/0.07;
mSF[1] = 0.;
mSF[2] = 0.;
Vertex z=0
50K/per particle type

Note: for BEMC ideal pedSigma set to 0, so effectively
there is no effect when I apply 3-sigma threshold above pedestal.

Figure 1: Energy sampling of various cluster in the Barrel EMC
E_reco is the total energy in the BEMC towers from mMuDstMaker->muDst()->muEmcCollection()
eta_thrown - rapidity of the thrown photon from the Geant record
Cuts: |eta| < 0.97 && |eta|>0.01 && total energy s.f. > 0.055

Figure 2: Various cluster energy ratios

2009.12.07 Low EM study: LOW_EM option, 100KeV cuts, and DCUTE=100KeV

Conclusions/dicsussion at the emc2 hypernew
http://www.star.bnl.gov/HyperNews-star/get/emc2/3369.html
http://www.star.bnl.gov/HyperNews-star/get/emc2/3375.html

Monte-Carlo setup:

Throwing one photon per event
Full STAR y2006h (latest EEMC, v6.1 and TPC, v04 geometries)
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and energy (5-35 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

GEANT EM cuts list (default values in GeV)

CUTGAM - cut for gammas (GEANT default = 0.001)
CUTELE - cut for electrons (GEANT default = 0.001)
CUTHAD - cut for charged hadrons (GEANT default = 0.01)
CUTNEU - cut for neutral hadrons (GEANT default = 0.01)
CUTMUO - cut for muons (GEANT default = 0.01)
BCUTE - cut for electron brems (GEANT default = CUTGAM)
BCUTM - cut for muon brems (GEANT default = CUTGAM)
DCUTE - cut for electron delta-rays (GEANT default = 10^4)
DCUTM - cut for muon delta-rays (GEANT default = 10^4)
LOSS - energy loss
STRA - energy fluctuation model
Birks law parameters (Tracking Parameters)
MODEL BIRK1; RKB BIRK2; C BIRK3

Low EM cut configurations (values in GeV)

NoCuts: Default STAR geometry EM cuts

Endcap EMC setup is quite non-uniform
(all cuts are set via "Call GSTPAR (ag_imed,'CutName', Value)":
- Block EMGT: 30 degree megatile
  CUTGAM = 0.00001
  CUTELE = 0.00001
- Block ESCI: active scintillator (polystyrene) layer
  CUTGAM = 0.00008
  CUTELE = 0.001
  BCUTE = 0.0001
  CUTNEU = 0.001
  CUTHAD = 0.001
  CUTMUO = 0.001
  c-- Define Birks law parameters
  BIRK1 = 1.
  BIRK2 = 0.013
  BIRK3 = 9.6E-6
- Block ELED : lead absorber plate
  CUTGAM = 0.00008
  CUTELE = 0.001
  BCUTE = 0.0001
  CUTNEU = 0.001
  CUTHAD = 0.001
  CUTMUO = 0.001
- Block EALP: thin aluminium plate in calorimeter cell
  CUTGAM = 0.00001
  CUTELE = 0.00001
  LOSS = 1.
  STRA = 1.
- Block EHMS: defines the triangular SMD strips
  CUTGAM = 0.00008
  CUTELE = 0.001
  BCUTE = 0.0001
  c-- Define Birks law parameters
  BIRK1 = 1.
  BIRK2 = 0.0130
  BIRK3 = 9.6E-6
100KeV: All cuts are set to 100KeV
CUTGAM = 0.0001
CUTELE = 0.0001
BCUTE = 0.0001
BCUTM = 0.0001
DCUTE = 0.0001
DCUTM = 0.0001
DCUTE: All cuts are set to 10KeV, except electron delta-rays (DCUTE = 100KeV)
CUTGAM = 0.00001
CUTELE = 0.00001
BCUTE = 0.00001
BCUTM = 0.00001
DCUTE = 0.0001
DCUTM = 0.00001
LOW_EM: All cuts are set to 10KeV
CUTGAM = 0.00001
CUTELE = 0.00001
BCUTE = 0.00001
BCUTM = 0.00001
DCUTE = 0.00001
DCUTM = 0.00001

Figure 1: Endcap EMC sampling fraction for different cluster sizes:
1x1, 2x1, 3x3, and total energy in the EEMC
Lower right plot shows total s.f. vs. photon thrown energy

Figure 2: Endcap EMC shower shapes

Figure 3: Endcap EMC shower shape ratios

2009.12.08 Low EM timing study: 10KeV vs. 100KeV cut settings

Conclusions/dicsussion at the emc2 hypernew:
http://www.star.bnl.gov/HyperNews-star/get/emc2/3374.html

List of LOW_EM cuts and defaults

Low EM cut configurations (values in GeV)

NoCuts: Default STAR geometry EM cuts
LOW_EM:100KeV: All LOW_EM cuts are set to 100KeV
LOW_EM:10KeV: (default) LOW_EM cuts (10KeV)
DCUTE: All cuts are set to 10KeV, except for electron delta-rays DCUTE = 100KeV

QCD hard processes timing

Pythia QCD Monte-Carlo:

Pythia pp@500GeV 2->2 hard QCD processes for parton pt>15GeV
Full STAR y2009a (latest EEMC, v6.1 and TPC, v04 geometries)
50 events per file, 100 jobs
BFC options and kumac details are here

Figure 1: QCD Total (GEANT/GSTAR+bfc) timing (seconds/event)

Figure 2: QCD GEANT/GSTAR timing (seconds/event)

Figure 3: QCD bfc.C timing (seconds/event)

EEMC single photon timing

EEMC single photons Monte-Carlo

One photon per event
Full STAR y2006h (latest EEMC, v6.1 and TPC, v04 geometries)
flat in eta (1.08, 2.0), phi (0, 2pi), and energy (5-35 GeV)
250 events per file, 200 jobs

Figure 4: EEMC single photon Total (GEANT/GSTAR+bfc) timing (seconds/event)

Figure 5: EEMC single photon GEANT/GSTAR timing (seconds/event)

Figure 6: EEMC single photon bfc.C timing (seconds/event)

2009.12.17 Ecalgeo-v6.2: embedded LOW_EM cuts in the calorimeter geometry

Monte-Carlo setup:

Throwing one photon per event
Full STAR y2006h (latest EEMC-v6.2 and BEMC with LOW_EM cuts, rest of geometry from CVS)
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and energy (5-35 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Figure 1: (left) Endcap EMC sampling fraction (total calorimeter energy), (right) SMD-u sampling fraction
Red: (previous) ecalgeo-v6.1 with global LOW_EM option
(Note: same points as in this post, Fig. 1 lower left, label y6:LOW_EM)
Black: (new) ecalgeo-v6.2 (embedded LOW_EM cuts), no global LOW_EM option

Figure 2: Pre-shower migrations
There is only a few events with pre1>4MeV with new simulations: potential problem with TPC geometry?

2009.12.20 Ecalgeo-v6.2: embedded LOW_EM cuts after TPC/EEMC overlap fix

Monte-Carlo setup:

Throwing one photon per event
Full STAR y2006h (latest EEMC-v6.2 and BEMC with LOW_EM cuts, rest of geometry from CVS)
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and energy (5-35 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Results: Update for the previous tests of EMC v6.2 geometry after fixing TPC/EEMC overlap

Figure 1: Endcap EMC sampling fraction: total calorimeter energy, pre1-, pre2-, post- shower layers, and SMD-u energy
Red: (previous) ecalgeo-v6.1 with global LOW_EM option
(Note: same points as in this post, Fig. 1 lower left, label y6:LOW_EM)
Black: (new) ecalgeo-v6.2 (embedded LOW_EM cuts), no global LOW_EM option

Figure 2: Pre-shower migrations
Change in TPC geometry seems to introduce a reasonable (small) change in pre-shower migration

Photon-jet simulation request

Simulation needs with y2006/y2009 geometry
specific to the photon-jet analysis

* Update version of the previous simulation
request from December 18, 2008 (see Ref. [1])

Understanding effects of trigger, material budget differences,
and throughout comparison between 2006 and 2009 data
requires to have dedicated Monte-Carlo
data samples with both y2006 and y2009 geometries.

Requested samples

We request to produce the following set of
Monte-Carlo samples for the photon-jet analysis:

S1: 1st priority
Dedicated (gamma filtered, Refs. [2-5]) data sample
for Pythia pp@200GeV prompt photon processes
with y2006 STAR geometry configuration
and partonic pt range 2-25GeV.

Simulations configured with:
- LOW_EM option in starsim (Ref. [6]).
  Low cuts on electromagnetic processes in GSTAR
- y2006h geometry tag, which includes
  latest Endcap EMC (v6.1) and TPC (v4) geometry fixes.
- Pythia 6.4 CDF Tune A or Perugia tunes (6.4.22)?
S2: 1st priority
Dedicated (gamma filtered, Refs. [2-5]) data sample
for Pythia pp@200GeV hard QCD processes
with y2006 STAR geometry configuration
and partonic pt range 2-25GeV.

Same simulation setup as for the sample S1.
S3: 2nd priority
Pythia pp@200GeV prompt photon and hard QCD
processes with y2009 STAR geometry configuration
and partonic pt range 2-25GeV.

Same simulation setup as for the sample S1
but with y2009a geometry tag.
S4: 3rd priority

Pythia pp@500GeV prompt photon and hard QCD
processes with y2009 STAR geometry configuration
and partonic pt range 2-25GeV.

Same simulation setup as for the sample S1
but with y2009a geometry tag.

Event number, CPU time, and disk space estimates

Below I provide some estimates of CPU and disk space
which are required to produce the data samples listed above.
These estimates are based on the previous (private)
production of the MC gamma-filtered events with y2006
geometry which was done at MIT computer cluster
by Michael Betancourt (Ref. [2,4-5]):

E1 (prompt photons)
Pythia pp@200GeV prompt photon simulations
with ~7 pb^-1 luminosity:
- ~60 days running time on a single CPU
- ~17Gb of disk space to store MuDst/geant files
- Number of (filtered) events:
  ~ 30K for pt range 6-9GeV
  ~ 15K for pt range 9-15GeV
E2 (QCD hard process)
Pythia pp@200 QCD hard process simulations
with (at least) 1 pb^-1 luminosity:
- ~ 620 days running on a single CPU
  (less than a week on a cluster with 100 CPUs)
- ~ 150Gb of disk space to store MuDst/geant files
- Number of (filtered) events:
  ~ 650K for pt range 6-9GeV
  ~ 300K for pt range 9-15GeV

Notes on the estimates:

N1
Enabling LOW_EM option in GSTAR increases
the time estimates by ~40% (Ref. [7]).
N2
Additional production of jet trees will
require a disk space on the order of < 2%
of the total size of the MuDst/geant files.
N3
Additional production of gamma trees will also
require a disk space on the order of a few percents
of the total size of the MuDst/geant files.

References

Previous simulation request (Date: 2008, Dec 18):
http://www.star.bnl.gov/HyperNews-star/protected/get/starspin/3596.html
Michael's document on
"Targeted MC procedure for the gamma-jet program at STAR":
http://drupal.star.bnl.gov/STAR/system/files/20080729_gammaFilter_by_MichaelBetancourt.pdf
simulations with filtering readiness:
http://www.star.bnl.gov/HyperNews-star/protected/get/starsimu/387/1/1/2/1/1/2/3/1.html
Filtered photon production with y2006 geometry:
http://www.star.bnl.gov/HyperNews-star/protected/get/phana/256.html
More details on statistics needed and disk space estimates:
http://www.star.bnl.gov/HyperNews-star/protected/get/phana/297.html
LOW_EM option in GSTAR:
http://www.star.bnl.gov/HyperNews-star/protected/get/phana/371.html
Time estimates with and without LOW_EM option:
- Barrel: http://drupal.star.bnl.gov/STAR/blog/betan/2009/nov/08/tuning-geant-and-bemc-sampling-fraction
- Endcap: http://drupal.star.bnl.gov/STAR/node/16177

2010

Year 2010 posts

01 Jan

January 2010 posts

2010.01.04 y2006 vs y2009 EEMC pre-shower migration

Monte-Carlo setup:

Throwing one photon per event
Full STAR y2006h/y2009a
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and energy (5-35 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Geometry configurations:

y6h:v6.2:10KeV y2006h after fixing TPC/EEMC overlap (CVS:2009/12/17)
y6h:10KeV y2006h Mising TPC material bug is resolved (CVS:2009/12/29)
y9a:10KeV y2009a after Mising TPC material bug is resolved (CVS:2009/12/29)

Note: results are with CVS before "15Deg rotated volume" bug being fixed

Figure 1:Pre-shower migration: y2006h (red - CVS:2009/12/17) vs. y2006h (black - CVS:2009/12/29)

Figure 2: Pre-shower migration: y2006h (red) vs. y2009a (black) all with CVS:2009/12/29

2010.01.07 EEMC response to single photons with y2006h vs y2009a geometries

Monte-Carlo setup

Throwing one photon per event
Full STAR y2006h/y2009a configurations
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and energy (5-35 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Geometry configurations:

y6h:10KeV (black) - y2006h with emc_10KeV
y9a:10KeV (red) - y2009a with emc_10KeV

STAR geometry includes the latest "15Deg rotated volume" bug bug fix

Figure 1: EEMC sampling fraction
(left) vs. thrown photon energy (with 1.2 < eta < 1.9 cut)
(right) vs. thrown photon eta

Figure 2: 2x1/3x3 clustering

Figure 3: Shower shapes (u plane)

Figure 4: Shower shape ratios (u plane)

Figure 5: Pre-shower migration (1.2 < eta < 1.9)

Figure 6: Average pre-shower1 energy vs. thown photon position in EEMC
(left) y2009a with emc_10KeV
(right) y2006h with emc_10KeV

2010.01.08 EEMC response 2006 vs. 2009: phi cuts

EEMC migration plots with cuts on TPC sector boundaries

Click here for results before phi cuts

Monte-Carlo setup

Throwing one photon per event
Full STAR y2006h/y2009a configurations
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and energy (5-35 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Geometry configurations:

y6h:10KeV (black) - y2006h with emc_10KeV option
y9a:10KeV (red) - y2009a with emc_10KeV option

Figure 1: Average pre-shower1 energy vs. thown photon position in EEMC
with cuts on TPC sector boundaries: cos(12*(phi-Pi/6.)) < -0.65 (similar plot before phi cuts)
(left) y2009a with emc_10KeV
(right) y2006h with emc_10KeV

Figure 2: Pre-shower 1 sampling fraction (E_pre1/E_thrown) vs. thrown eta

Figure 3: EEMC sampling fraction
(left) vs. thrown photon energy (with 1.2 < eta < 1.9 cut)
(right) vs. thrown photon eta

Figure 4: Pre-shower migration (1.2 < eta < 1.9)

2010.01.12 W test sample QA

All plost from second (with vertex distribution) test W-sample from Lidia/Jason.
generated files are from /star/rcf/test/Wprod_test2/

The previous sample with fixed (zero) vertex can be found was announced here:
http://www.star.bnl.gov/HyperNews-star/protected/get/starsimu/435.html

Figure 1: Electron from W decay (a) eta, (b) phi, (c) pt and (d) energy distributions
from geant record (no kinematic cuts)

Figure 2: Reconstructed vs. geant vertex (Cuts: abs(geant_eta_electron) < 1)
(left) difference, (right) ratio

Figure 3:
(left) Correlation between thrown and reconsructed energy: abs(geant_eta_electron) < 1
(right) ratio of the reconsructed to thrown energy (Bemc_Etow > 25)
Reconstructed energy is the total energy in all Barrel towers

Data base setup

The follwoing DB tables are used to read MuDst (dbMk->SetDateTime(20090325,0)):

StEmcSimulatorMaker:INFO - loaded a new bemcPed table with beginTime 2009-03-24 22:16:13 and endTime 2009-03-26 06:03:44
StEmcSimulatorMaker:INFO - loaded a new bemcStatus table with beginTime 2009-03-24 02:16:58 and endTime 2009-03-26 04:07:02
StEmcSimulatorMaker:INFO - loaded a new bemcCalib table with beginTime 2008-12-15 00:00:02 and endTime 2037-12-31 12:00:00
StEmcSimulatorMaker:INFO - loaded a new bemcGain table with beginTime 1999-01-01 00:08:00 and endTime 2037-12-31 12:00:00
StEmcSimulatorMaker:INFO - loaded a new bprsPed table with beginTime 2008-03-04 10:30:56 and endTime 2037-12-31 12:00:00
StEmcSimulatorMaker:INFO - loaded a new bprsStatus table with beginTime 2008-12-15 00:00:00 and endTime 2037-12-31 12:00:00
StEmcSimulatorMaker:INFO - loaded a new bprsCalib table with beginTime 1999-01-01 00:10:00 and endTime 2037-12-31 12:00:00
StEmcSimulatorMaker:INFO - loaded a new bprsGain table with beginTime 1999-01-01 00:08:00 and endTime 2037-12-31 12:00:00
StEmcSimulatorMaker:INFO - loaded a new bsmdePed table with beginTime 2009-03-24 15:42:29 and endTime 2009-03-25 11:24:55
StEmcSimulatorMaker:INFO - loaded a new bsmdeStatus table with beginTime 2009-03-24 15:42:29 and endTime 2009-03-25 11:24:55
StEmcSimulatorMaker:INFO - loaded a new bsmdeCalib table with beginTime 2002-11-14 00:01:00 and endTime 2037-12-31 12:00:00
StEmcSimulatorMaker:INFO - loaded a new bsmdeGain table with beginTime 1999-01-01 00:08:00 and endTime 2037-12-31 12:00:00
StEmcSimulatorMaker:INFO - loaded a new bsmdpPed table with beginTime 2009-03-24 15:42:29 and endTime 2009-03-25 11:24:55
StEmcSimulatorMaker:INFO - loaded a new bsmdpStatus table with beginTime 2009-03-24 15:42:29 and endTime 2009-03-25 11:24:55
StEmcSimulatorMaker:INFO - loaded a new bsmdpCalib table with beginTime 2002-11-14 00:01:00 and endTime 2037-12-31 12:00:00
StEmcSimulatorMaker:INFO - loaded a new bsmdpGain table with beginTime 1999-01-01 00:08:00 and endTime 2037-12-31 12:00:00
StEmcSimulatorMaker:INFO - loaded a new bemcTriggerStatus table with beginTime 2009-03-23 07:50:04 and endTime 2009-04-01 18:10:03
StEmcSimulatorMaker:INFO - loaded a new bemcTriggerPed table with beginTime 2009-03-20 04:11:43 and endTime 2009-03-30 20:00:05
StEmcSimulatorMaker:INFO - loaded a new bemcTriggerLUT table with beginTime 2009-03-23 07:50:04 and endTime 2009-04-03 22:08:11

2010.01.13 W test sample QA: Pass 2

http://drupal.star.bnl.gov/STAR/node/16704

QA of the test W-sample from Lidia/Jason.
generated MuDst are from /star/simu/jwebb/01-11-2010-w-test-production/

QA plots for the previous pass can be found here

Two channels being analyzed:

wtest10000 W+ --> e+ nu (shown by black line)
wtest10001 W- --> e- nu (shown by red line)

Cuts: |geant_eta_lepton| < 1

Discussions can be found here:
http://www.star.bnl.gov/HyperNews-star/protected/get/starsimu/440.html

Figure 1: (left) Reconstructed vertex z distribution
(right) reconstructed minus geant z-vertex

Figure 2: E2x2/E_geant energy ratio
Black: positron from W+, mean value= 0.972973;
Red - electron from W- mean value = 0.969773

Figure 3: E1x1/E_geant (highest tower) energy ratio
Black: positron from W+, mean value= 0.815287;
Red - electron from W- mean value = 0.812098

Update on Jan 14, 2010

Figure 4: Lepton E2x2/E_geant energy ratio

Parameter	black: positron from W+	red: electron from W-
gaus-Constant	1.60709e+01 , err=3.08565e+00	1.56834e+01 , err=4.71967e+00
gaus-Mean	9.85514e-01 , err=4.94309e-03	9.86118e-01 , err=5.43577e-03
gaus-Sigma	3.15205e-02 , err=3.73952e-03	2.52009e-02 , err=6.57793e-03
Hist-Mean	0.972973	0.969773

Figure 5: Lepton E3x3/E_geant energy ratio

Parameter	black: positron from W+	red: electron from W-
gaus-Constant	1.48719e+01 , err=2.82186e+00	1.35741e+01 , err=3.36776e+00
gaus-Mean	9.89924e-01 , err=5.72959e-03	9.83056e-01 , err=6.28736e-03
gaus-Sigma	3.37758e-02 , err=4.24983e-03	3.00597e-02 , err=6.06841e-03
Hist-Mean	0.975662	0.974163

2010.01.15 W test sample QA: Pass 3

QA of the test W-sample from Lidia/Jason.
generated MuDst are from /star/data08/users/starreco/recowtest/

QA plots for the previous pass 2 can be found here

QA plots for the previous pass 1 can be found here

Two channels being analyzed:

wtest10000 W+ --> e+ nu (shown by black line)
wtest10001 W- --> e- nu (shown by red line)

Cuts: |geant_eta_lepton| < 1

Discussions can be found here:
http://www.star.bnl.gov/HyperNews-star/protected/get/starsimu/443.html

Figure 1: Reconstructed minus geant z-vertex

Figure 2: Lepton E2x2/E_geant energy ratio

Parameter	black: positron from W+	red: electron from W-
gaus-Constant	6.24023e+01 , err=3.46979e+00	4.73536e+01 , err=3.16029e+00
gaus-Mean	9.79982e-01 , err=1.69854e-03	9.79787e-01 , err=1.68813e-03
gaus-Sigma	3.52892e-02 , err=1.40963e-03	3.15336e-02 , err=1.40759e-03
Hist-Mean	0.972122	0.975073

Figure 3: Lepton E3x3/E_geant energy ratio

Parameter	black: positron from W+	red: electron from W-
gaus-Constant	6.33596e+01 , err=3.58862e+00	4.72335e+01 , err=3.19552e+00
gaus-Mean	9.83287e-01 , err=1.72276e-03	9.83632e-01 , err=1.67661e-03
gaus-Sigma	3.45514e-02 , err=1.48019e-03	3.05224e-02 , err=1.38944e-03
Hist-Mean	0.974372	0.977858

2010.01.18 EEMC response 2006 vs. 2009: typo in TPC volume size bug fix

Click here for previous study before TPC typo fix

Monte-Carlo setup

Throwing one photon per event
Full STAR y2006h/y2009a configurations
Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and energy (5-35 GeV)
Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain)
Vertex z=0
~50K/per particle type
Non-zero energy: 3 sigma above pedestal

Before and after the fix comparison

Geometry configurations:

y6h:10KeV:old (red) - y2006h with emc_10KeV option
y6h:10KeV (black) - y2006h with emc_10KeV option, after TPC typo fixed

Figure 1: Pre-shower migration (1.2 < eta < 1.9)

y2006h vs. y2009a comparison after a TPC typo fix

Geometry configurations:

y9a:10KeV (red) - y2009a with emc_10KeV option
y6h:10KeV (black) - y2006h with emc_10KeV option

Figure 2: Pre-shower migration (1.2 < eta < 1.9): y2006h vs. y2009a

2010.01.18 W test sample: Cluster ratios and skewed gaussian fits

W test sample from Lidia/Jason. MuDst's from /star/data08/users/starreco/recowtest/

Two channels being analyzed:

wtest10000 W+ -> e+ nu
wtest10001 W- -> e- nu

Figure 1: Lepton yield vs. rapidity (no cuts)

Figure 2: Lepton yield vs. pt and energy
(left) no rapidity cuts
(right) |lepton_eta| < 1

Cluster energy vs. original lepton energy

All plots below with |lepton_eta| < 1

Skewed gaussian fits: [const]*exp(-0.5*((x-[mean])/([sigma]*(1+[skewness]*(x-[mean]))))**2)

Figure 3: Lepton E1x1/E_geant energy ratio

Figure 4: Lepton E2x2/E_geant energy ratio

Figure 5: Lepton E3x3/E_geant energy ratio

2010.01.26 Endcap/Barrel clustering with official W-MC

Simulations: official pp 500GeV pythia W production

Two channels being analyzed:

W+ -> e+ nu (rcf10010*.root)
W- -> e- nu (rcf10011*.root)

Lepton from W in the Endcap: 1.2 < eta_lepton < 1.9

Figure 1: Lepton yield vs. energy

Figure 2: Lepton (left) E1x1/E_thrown and (right) E2x2/E_thrown energy ratio
Skewed gaussian fits: [const]*exp(-0.5*((x-[mean])/([sigma]*(1+[skewness]*(x-[mean]))))**2)

Figure 3: Endcap EMC lepton E3x3/E_thrown energy ratio

Figure 4: Endcap 2x2 sampling fraction (s.f.) vs. thrown lepton (left) energy and (right) eta
S.f. is defined as an average E_2x2/E_thrown for E_2x2/E_thrown>0.8

Figure 5: Endcap 3x3 sampling fraction (s.f.) vs. thrown lepton (left) energy and (right) eta
S.f. is defined as an average E_3x3/E_thrown for E_3x3/E_thrown>0.8

Lepton from W in the Barrel: |eta_lepton| <1

Figure 6: Lepton yield vs. energy

Figure 7: Lepton (left) E1x1/E_thrown and (right) E2x2/E_thrown energy ratio

Figure 8: Barrel EMC lepton E3x3/E_thrown energy ratio

Figure 9: Barrel 2x2 s.f. vs. thrown lepton (left) energy and (right) eta
S.f. is defined as an average E_2x2/E_thrown for E_2x2/E_thrown>0.8

Figure 10: Barrel 3x3 s.f. vs. thrown lepton (left) energy and (right) eta
S.f. is defined as an average E_3x3/E_thrown for E_3x3/E_thrown>0.8

02 Feb

February 2010 posts

2010.02.08 StEemcGammaFilterMaker QA: QCD vs. gamma-jet ccept/reject rates

StEemcGammaFilterMaker QA

Pythia generated processes

Pythia gamma-jet	Pythia QCD 2->2 processes
14 f + fbar -> g + gamma	11 f + f' -> f + f' (QCD)
18 f + fbar -> gamma + gamma	12 f + fbar -> f' + fbar'
29 f + g -> f + gamma	13 f + fbar -> g + g
114 g + g -> gamma + gamma	28 f + g -> f + g
115 g + g -> g + gamma	53 g + g -> f + fbar
	68 g + g -> g + g

Number of generated events per parton pt bin

	Number of generated events (100events/job)
Parton pt range (GeV)	2-3	3-4	4-6	6-9	9-15	15-25
Pythia gamma-jet	50K	50K	50K	50K	50K	50K
Pythia QCD 2->2 processes	100K	100K	50K	50K	50K	50K

Filter configuration

Filter parameter	Value	Notes
mConeRadius	0.24
mSeedThreshold	2.5	Cluster seed energy threshold
mClusterThreshold	3.7	Cluster Et threshold
mEtaLow	0.9	EEMC acceptance
mEtaHigh	2.1	EEMC acceptance
mSmearEnergy	0	Disabled
mThrowTracks	0	Disabled
mCalDepth	279.5	ZDC SMD depth
mMinPartEnergy	1e-05	Disabled by mThrowTracks=0
mHadronScale	0.4	Downscale factor for hadron energy
mFilterMode	0	Accepting all events

stasim/Pythia options

detp geometry y2006h
Calorimeter cut for electromagnetic processes: emc_10keV
call pytune(100): PYTUNE v1.015; CDF Tune A

Figures

Figure 1: Pythia Eemc gamma filter QA:
(left) False rejection, (right) fraction of accepted events

Comments

Overall false rejection is < 0.02% (10^-4)
for both QCD and gamma-jet simulations.
In parton pt range 6-15 GeV the acceptance rate
for the gamma-jet MC is ~ 50%
(I think this is due to rapidity fiducial cut,
otherwise it should be closer to 100%).
For the QCD sample (in the same pt region)
acceptance rate is 8-25%.
Somehow for highest parton pt filter acceptance is the
same for gamma-jet and QCD Monte-Carlo.

2010.02.09 BFC and Pythia QA: relative to trigger

QCD and gamma-jet data samples are described here

Pythia filter configuration

StEemcGammaFilter:: running the TEST mode (accepting all events). Set mFilterMode=1 to actually reject events
StEemcGammaFilter:: mConeRadius 0.22 mSeedThreshold 2.1 mClusterThreshold 3.25 mEtaLow 0.95 mEtaHigh 2.1
StEemcGammaFilter:: mCalDepth 279.5 mMinPartEnergy 1e-05 mHadronScale 0.4 mFilterMode 0 mPrintLevel 0

BFC filter configuration

StChain:INFO - Init() : Seed energy threshold = 2.8 GeV
StChain:INFO - Init() : Cluster eT threshold = 4.2 GeV
StChain:INFO - Init() : Maximum vertex = +/- 120 cm
StChain:INFO - Init() : Running the TEST mode (accepting all events). Set mFilterMode=1 to actually reject events in BFC

Accept/Reject relative to the total number of Pythia generated events

Figure 1: Fraction of accepted events

Figure 2: False rejection (Y-axis scale is 10^-3)

Accept/Reject relative to the number of triggered events

Figure 3: Fraction of accepted events (relative to triggered events)

Figure 4: False rejection relative to triggered events

2010.02.10 Money plots for W cross section

2010.02.11 BFC and Pythia QA: Gain no-gain-spread, mean=1.05

Click here for discussion and results with spread=0.05/gain=0.95

QCD and gamma-jet data samples are described here

Resultys without gain shift can be found here
(Note: ignore parton pt=25-35GeV for the gamma-jet sample since all jobs failed)

Pythia filter configuration

BFC filter configuration

Accept/Reject relative to the total number of Pythia generated events

Figure 1: Fraction of accepted events

Accept rate: fract. of generated events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.0023	0.06264	0.00148
pt=3-4	0.0242285	0.250601	0.0126854
pt=4-6	0.103111	0.427535	0.0571313
pt=6-9	0.16828	0.48368	0.13918
pt=9-15	0.1692	0.50118	0.1619
pt=15-25	0.12708	0.42904	0.11786
pt=25-35	0.05702	0.24854	0.0509
QCD
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	3.003e-05	0.00426426	2.002e-05
pt=3-4	0.0001001	0.0122923	1.001e-05
pt=4-6	0.00078	0.03166	0.00014
pt=6-9	0.00622	0.10538	0.00216
pt=9-15	0.02822	0.27666	0.01022
pt=15-25	0.07568	0.4405	0.03086
pt=25-35	0.0761	0.35556	0.04116

Figure 2: False rejection (Y-axis scale is 10^-3)

False reject: fract. of generated events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	2e-05	0	0
pt=3-4	2.00401e-05	0	0
pt=4-6	8.08081e-05	0	0
pt=6-9	6e-05	4e-05	0
pt=9-15	0.0002	0.00018	0
pt=15-25	0.0001	4e-05	0
pt=25-35	0.00018	2e-05	0
QCD
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0	0	0
pt=3-4	0	0	0
pt=4-6	0	0	0
pt=6-9	4e-05	6e-05	0
pt=9-15	4e-05	0.00026	0
pt=15-25	0.00016	0.00018	0
pt=25-35	4e-05	4e-05	0

Accept/Reject relative to the number of triggered events

Figure 3: Fraction of accepted events (relative to triggered events)

Figure 4: False rejection relative to triggered events

Accept/Reject relative to the number of Pythia filter accepted events

Figure 5: Fraction of accepted events relative to Pythia filter accepted events

Accept rate: fract. of Pythia-filtered events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.0363985	1	0.0220307
pt=3-4	0.0966813	1	0.0501399
pt=4-6	0.241081	1	0.133488
pt=6-9	0.347461	1	0.287587
pt=9-15	0.336286	1	0.322639
pt=15-25	0.29573	1	0.274613
pt=25-35	0.228776	1	0.204635
QCD
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.00704225	1	0.00234742
pt=3-4	0.00488599	1	0.000814332
pt=4-6	0.0214782	1	0.00442198
pt=6-9	0.053141	1	0.0191687
pt=9-15	0.0965806	1	0.0356394
pt=15-25	0.16958	1	0.0695573
pt=25-35	0.213185	1	0.115592

Figure 6: False rejection relative to Pythia filter accepted events

False reject: fract. of Pythia-filtered events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.000319285	0	0
pt=3-4	7.9968e-05	0	0
pt=4-6	0.000189009	0	0
pt=6-9	0.000124049	0	0
pt=9-15	0.000399058	0	0
pt=15-25	0.000233079	0	0
pt=25-35	0.000724229	0	0
QCD
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0	0	0
pt=3-4	0	0	0
pt=4-6	0	0	0
pt=6-9	0.000379579	0	0
pt=9-15	0.000144582	0	0
pt=15-25	0.000363224	0	0
pt=25-35	0.000112499	0	0

2010.02.11 BFC and Pythia QA: Gain spread=0.05, mean=0.95

QCD and gamma-jet data samples are described here

Resultys without gain spread can be found here
(Note: ignore parton pt=25-35GeV for the gamma-jet sample since all jobs failed)

Gain spread implementation in StEEmcSlowMaker.cxx (private version):

void StEEmcSlowMaker::setTowerGainSpread(Float_t s, Float_t mTowerGainMean)
{
LOG_INFO << "setTowerGainSpread(): gain spread: " << s << "; gain mean value: " << mTowerGainMean << endm;
// initialize tower gain factors to 1
for ( Int_t sec=0;sec<kEEmcNumSectors;sec++ )
    for ( Int_t sub=0;sub<kEEmcNumSubSectors;sub++ )
      for ( Int_t eta=0;eta<kEEmcNumEtas;eta++ )
    {
    //    mTowerGainFact[sec][sub][eta]=1.0;

    Float_t f = -1.0E9;
    while ( f <= -1. || f >= 1.0 )
        f = gRandom->Gaus(0., s);

    mTowerGainFact[sec][sub][eta] = mTowerGainMean + f;

    }
}

Pythia filter configuration

BFC filter configuration

Accept/Reject relative to the total number of Pythia generated events

Figure 1: Fraction of accepted events

Figure 2: False rejection (Y-axis scale is 10^-3)

Accept/Reject relative to the number of triggered events

Figure 3: Fraction of accepted events (relative to triggered events)

Figure 4: False rejection relative to triggered events

2010.02.12 Final Pythia and BFC EEMC-gamma-filter paramter settings

Pythia generated processes

Prompt photons (gamma-jets)		2->2 QCD
id	Process	id	Process
14	f + fbar -> g + gamma	11	f + f' -> f + f' (QCD)
18	f + fbar -> gamma + gamma	12	f + fbar -> f' + fbar'
29	f + g -> f + gamma	13	f + fbar -> g + g
114	g + g -> gamma + gamma	28	f + g -> f + g
115	g + g -> g + gamma	53	g + g -> f + fbar
		68	g + g -> g + g

Number of generated events per parton pt bin

	Number of generated events (100events/job)
Parton pt range (GeV)	2-3	3-4	4-6	6-9	9-15	15-25	25-35
gamma-jets	50K	50K	50K	50K	50K	50K	50K
2->2 QCD processes	100K	100K	50K	50K	50K	50K	50K

Pythia Filter configuration

StRoot/StMCFilter/StEemcGammaFilter.cxx
StRoot/StMCFilter/StEemcGammaFilter.h

Filter parameter	Value	Notes
mConeRadius	0.22
mSeedThreshold	2.6	Cluster seed energy threshold (GeV)
mClusterThreshold	3.6	Cluster Et threshold (GeV)
mEtaLow	0.95	EEMC acceptance
mEtaHigh	2.1	EEMC acceptance
mMaxVertex	120.0	Vertex z cut (cm)
mCalDepth	279.5	EEMC SMD depth (cm)
mMinPartEnergy	1e-05	Ignore track with minim energy (GeV)
mHadronScale	0.4	Downscale factor for hadron energy
mFilterMode	0 / 1	0=Accept all events; 1=reject events

BFC Filter configuration

StRoot/StFilterMaker/StEemcGammaFilterMaker.cxx
StRoot/StFilterMaker/StEemcGammaFilterMaker.h

Filter parameter	Value	Notes
mSeedEnergyThreshold	3.4	Cluter seed energy threshold (GeV)
mClusterEtThreshold	4.5	Cluster Et threshold (GeV)
mEemcSamplingFraction	0.05	Assume 5% sampling fraction for EEMC
mMaxVertex	120.0	Vertex z cut (cm)
mFilterMode	0 / 1	0=Accept all events; 1=reject events

GammaMaker configuration

Filter parameter	Value	Notes
ConeRadius	0.7
ClusterEtThreshold	5.5	(GeV)
SeedEnergyThreshold	4.2	(GeV)
ClusterEnergyThreshold	5.5	(GeV)

EEMC SlowSimulator configuration

(for a moment private version) of StEEmcSlowMaker.cxx
with modified setTowerGainSpread(Float_t s, Float_t mTowerGainMean)

Filter parameter	Value	Notes
mTowerGainMean	1.05	Overall +5% gain shift
GainSpread	0	No gain spread

GSTAR/Pythia options

detp geometry y2006h
Calorimeter cut for electromagnetic processes: emc_10keV
call pytune(100): PYTUNE v1.015; CDF Tune A
or
call pytune(320): PYTUNE Perugia; Perugia 0 tune

2010.02.16 Pythia and BFC filter QA vs. gamma candidate pt and eta

QCD and gamma-jet data samples and filter configurtions are given here

Note: for this study I have used ideal EEMC gains (no gain shift/spread)

Note on trigger effect intepretation:
There is no requirement for the Pythia gamma-jet sample to have direct gamma
headed to the EEMC, only requirement is to have a gamma candidate in the EEMC,
so the away side jet may also contribute.

Figure 1: pt distribution of the gamma candidates
for Pythia/Bfc level filter and triggered events
Event cuts: at least one gamma candidate, |v_z| <120
Left: Pythia gamma-jet MC; (right) 2->2 Pythia QCD
Lower plots: fraction of accepted gamma candidates by filter/trigger
No parton pt weights (= ignore bumps in pt distribution for gamma-jet sample)

Figure 2: Rapidity distribution of the gamma candidates (Same conditions as in Fig. 1)

Figure 3: pt distribution of false rejection for Pythia/Bfc filters
Candidate cuts: at least one gamma candidate, l2gamm-trigger=fired, |v_z| <120
Most of false rejection (~ 1-3% for QCD) is for gamma candidates with pt < 8GeV

03 Mar

March 2010 posts

2010.03.02 Endcap photon-jets simulation request (draft)

Request last updated on Jul 21, 2010

Request summary

Total resources estimate for QCD with 1/pb and prompt-photon with 10/pb suimulations:

CPU: 4.2 CPU years (2.2 weeks of running on a 100 CPUs)
Disk space: 0.15Tb
Numbe of filtered events: 0.74M

partonic pt	QCD	prompt photon
2-3	0	30K
3-4	0	36K
4-6	130K	76K
6-9	240K	40K
9-15	150K	10K
15-35	23K	1K

Latest filter bias/timing test and simulation request spreasheet

Note: These and all other studies are linked from here

Filter code in cvs

Pythia gamma-filter code:
StEemcGammaFilter.cxx
StEemcGammaFilter.h
BFC gamma-filter code:
StEemcGammaFilterMaker.cxx
StEemcGammaFilterMaker.h
eemcGammaFilterMakerParams.idl

Further information related to this request

Lidia added "y2006h" tag (latest Endcap geometry fixes, and Calorimeters with LOW_EM cuts)
http://www.star.bnl.gov/HyperNews-star/protected/get/starsimu/452/1.html
x/y beam offset:
Run 6: x=0.0cm, y=-0.3cm (from /STAR/comp/calib/Beamline/Run6)
Run 9: x=0.3cm, y= 0.0cm (from /STAR/comp/calib/BeamLine/Run9)
Vertex z cut:
+/- 120cm
Vertex z spread:
    Run 6: 55cm (gaus fit to Fig.1 from this post: /STAR/node/13276)
    Run 9: 63cm are taken from Pibero's embedding study:
    www4.rcf.bnl.gov/~pibero/spin/dijets/2009.10.23/embedding.html
Vertex x/y spread set to zero for all runs.
FYI, Run 9 x/y spread is x=0.57, y=0.58
http://www4.rcf.bnl.gov/~pibero/spin/dijets/2009.10.23/XYVertexJetTriggers.png
Vertex option:
    Use option consistent with bfc tags used for data production (VFPPV/Run-6 or VFPPVnoCTB/Run-9):
       Run 6: Leave vertex to be reconstructed vertex, and use VFPPV with beamline
   Run 9: Leave vertex to be reconstructed vertex, and use VFPPVnoCTB with beamline

FYI: bfc options for different years:
http://www.star.bnl.gov/devcgi/dbProdOptionRetrv.pl
Use the latest available "SLXXy" library tag
No sdt option for bfc with Monte-Carlo. See note from Jreome's:
http://www.star.bnl.gov/HyperNews-star/protected/get/starsoft/7905/1/1/2.html
Need to add new bfc tag. Request send to starsimu list:
http://www.star.bnl.gov/HyperNews-star/protected/get/starsimu/453.html
Using Perugia0 tunes (i.e. call pytune(320))
GMT timestamp update
http://www.star.bnl.gov/HyperNews-star/protected/get/phana/481.html

Run 6 200 GeV
sdt20060512.043500 (GMT during run 7132005)
sdt20060513.064000 (GMT during run 7133011)
sdt20060514.090000 (GMT during run 7134015)
sdt20060516.152000 (GMT during run 7136022)
sdt20060518.073700 (GMT during run 7138010)
sdt20060520.142000 (GMT durign run 7140024)
sdt20060521.052000 (GMT during run 7141011)
sdt20060522.124500 (GMT during run 7142029)
sdt20060523.204400 (GMT during run 7143044)
sdt20060525.114000 (GMT during run 7145023)
sdt20060526.114000 (GMT during run 7146020)
sdt20060528.144500 (GMT during run 7148028)
sdt20060602.071500 (GMT during run 7153015)
sdt20060604.191200 (GMT during run 7155043)

Run 9 500 GeV
sdt20090320.014942
sdt20090321.095723
sdt20090324.064934
sdt20090328.040659
sdt20090329.014902
sdt20090404.194055
sdt20090407.030832
sdt20090410.020208
sdt20090411.103512
sdt20090413.021450

Run 9 200 GeV
std20090506.083000 (GMT during run 10126017)
std20090508.152000 (GMT during run 10128053)
std20090514.145500 (GMT during run 10134035)
std20090516.182500 (GMT during run 10135070)
std20090517.214000 (GMT during run 10137052)
std20090518.111600 (GMT during run 10138027)
std20090519.173200 (GMT during run 10139069)
std20090520.100500 (GMT during run 10140011)
std20090522.141000 (GMT during run 10142043)
std20090523.183500 (GMT during run 10143065)
std20090524.112000 (GMT during run 10144035)
std20090525.062000 (GMT during run 10145012)
std20090526.140000 (GMT during run 10146052)
FYI:
simulation request posted to SPIN PWG:
http://www.star.bnl.gov/HyperNews-star/protected/get/starspin/3982.html
Code status:
http://www.star.bnl.gov/HyperNews-star/protected/get/starsoft/8073.html
Code per review (by Pibero and Victor):
http://www.star.bnl.gov/HyperNews-star/protected/get/starsoft/8073/2.html
Note: code being approved

Original disk space estimate (see spreadsheed linked above for the latest estimates):

http://www.star.bnl.gov/HyperNews-star/protected/get/starspin/3982.html

------------------------ REQUEST DRAFT BELOW ----------------------------------------

Endcap photon-jets / QCD 2->2 simulations

Request TypeEvent generator simulation, with filtering

General Information


Request ID
Priority: EC	0
Priority: pwg	High
Status	New
Physics Working Group	Spin
Requested by	Photon group for SPIN PWG
Contact email(s)	ilya.selyuzhenkov@gmail.com, bridgeman@hep.anl.gov
Contact phone(s)
PWG email(s)	starspin-hn@www.star.bnl.gov
Assigned Deputy:	Not assigned
Assigned Helper:	Not assigned

Description

Endcap photon-jets request

Global Simulation Settings


Request type:	Event generator simulation, with filtering
Number of events	See list for each partonic pt bins
Magnetic Field	Run 6: Full-Field Run 9: Reversed Full-Field
Collision Type	Run 6: pp@200GeV Run 9: pp@200GeV Run 9: pp@500GeV
Centrality	---- SELECT CENTRALITY ----
BFC tags	Run 6: trs fss y2006h Idst IAna l0 tpcI fcf ftpc Tree logger ITTF Sti VFPPV bbcSim tofsim tags emcY2 EEfs evout -dstout IdTruth geantout big fzin MiniMcMk eemcDb beamLine clearmem Run 9: tpcrs y2009a MakeEvent ITTF NoSsdIt NoSvtIt Idst BAna l0 Tree logger Sti VFPPVnoCTB tpcDB TpcHitMover TpxClu bbcSim tofsim tags emcY2 EEfs evout IdTruth geantout big fzin McEvOut MiniMcMk eemcDb beamLine clearmem
Production	---- SELECT PRODUCTION TAG ----
Geometry: simu	Run 6: y2006h Run 9: y2009a
Geometry: reco	Run 6: y2006h Run 9: y2009a
Library	use library with approved filter code checked in
Vertex option	Run 6: Leave vertex to be reconstructed vertex, and use VFPPVnoCTB with beamline Run 9: Leave vertex to be reconstructed vertex, and use VFPPVnoCTB with beamline
Pileup option	No
Detector Set	Run 6: TPC, ETOW, BTOW, BSMD, ESMD, BPRS, EPRE1, EPRE2, EPOST, TOF, BBC, SVT, SSD Run 9: TPC, ETOW, BTOW, BSMD, ESMD, BPRS, EPRE1, EPRE2, EPOST, TOF, BBC

Data Sources

MC Event Generator


Event generator	Pythia
Extra options	Additional libraries required for Eemc-gamma Pythia-level filter gexec $ROOTSYS/lib/libCint.so gexec $ROOTSYS/lib/libCore.so gexec $ROOTSYS/lib/libMathCore.so gexec $ROOTSYS/lib/libMatrix.so gexec $ROOTSYS/lib/libPhysics.so gexec .sl53_gcc432/lib/StMCFilter.so // filter library Select prompt photon Pythhia processes: MSUB (14)=1 MSUB (18)=1 MSUB (29)=1 MSUB (114)=1 MSUB (115)=1 Select QCD 2->2 Pythhia processes: MSUB (11) = 1 MSUB (12) = 1 MSUB (13) = 1 MSUB (28) = 1 MSUB (53) = 1 MSUB (68) = 1 Perugia0 Pythia tune: call pytune(320)
Vertex Z, cm	-120 < Vertex < 120
Gaussian sigma in X,Y,Z if applicable	x/y spread use 0 Run 6: 0, 0, 55 200 GeV Run 9: 0, 0, 63 200 GeV Run 9: 0, 0, 42 500 GeV
Vertex offset: x, mm	Run 6: 0.0cm Run 9: 0.3cm (note: values in cm)
Vertex offset: y, mm	Run 6: -0.3cm (note: values in cm) Run 9: 0.0cm
Φ (phi), radian	0 < Φ < 6.29
η (eta)	Default (include Barrel, Endcap, BBC)
Pt bin, GeV	See list above for QCD and g-jet samples
Macro file	Pythia gamma-filter code: StEemcGammaFilter.cxx StEemcGammaFilter.h BFC gamma-filter code: StEemcGammaFilterMaker.cxx StEemcGammaFilterMaker.h eemcGammaFilterMakerParams.idl Private bfc: /star/u/seluzhen/star/spin/MCgammaFilter/scripts/bfc.C

04 Apr

April 2010 posts

2010.04.09 BFC and Pythia QA: 10% gain shift

See this post from Alice for QCD sample rates

QCD and gamma-jet data samples are described here (filter parameters are listed below)

Pythia filter configuration

StEemcGammaFilter:: running the TEST mode (accepting all events). Set mFilterMode=1 to actually reject events
StEemcGammaFilter:: mConeRadius 0.22 mSeedThreshold 2.6 mClusterThreshold 3.6 mEtaLow 0.95 mEtaHigh 2.1 mMaxVertex 120
StEemcGammaFilter:: mCalDepth 279.5 mMinPartEnergy 1e-05 mHadronScale 1 mFilterMode 0 mPrintLevel 1

BFC filter configuration

StChain:INFO - Init() : Using gamma filter on the EEMC
StChain:INFO - Init() : EEMC Sampling Fraction = 0.05
StChain:INFO - Init() : Seed energy threshold = 3.4 GeV
StChain:INFO - Init() : Cluster eT threshold = 4.5 GeV
StChain:INFO - Init() : Maximum vertex = +/- 120 cm
StChain:INFO - Init() : Running the TEST mode (accepting all events). Set mFilterMode=1 to actually reject events in BFC

StEEmcSlowMaker

BFC:INFO - setTowerGainSpread(): gain spread: 0; gain mean value: 1.1

Accept/Reject relative to the total number of Pythia generated events

Figure 1: Fraction of accepted events

Accept rate: fract. of generated events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.00646	0.09532	0.00656
pt=3-4	0.03042	0.2401	0.02772
pt=4-6	0.09438	0.42552	0.07568
pt=6-9	0.165984	0.54004	0.149137
pt=9-15	0.167329	0.559137	0.162972
pt=15-25	0.12486	0.45662	0.11744
pt=25-35	0.0562525	0.269499	0.0536273

Figure 2: False rejection (Y-axis scale is 10^-3)

False reject: fract. of generated events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	6e-05	0	0
pt=3-4	0.00016	6e-05	0
pt=4-6	0.0003	8e-05	0
pt=6-9	0.000261044	6.0241e-05	0
pt=9-15	0.000220884	6.0241e-05	0
pt=15-25	0.00028	0.00012	0
pt=25-35	0.000280561	0.000140281	0

Accept/Reject relative to the number of Pythia filter accepted events

Figure 3: Fraction of accepted events relative to Pythia filter accepted events

Accept rate: fract. of Pythia-filtered events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.0677717	1	0.0612673
pt=3-4	0.126697	1	0.105623
pt=4-6	0.221799	1	0.16991
pt=6-9	0.307318	1	0.26809
pt=9-15	0.299264	1	0.281882
pt=15-25	0.273444	1	0.244317
pt=25-35	0.20873	1	0.181217

Figure 4: False rejection relative to Pythia filter accepted events

False reject: fract. of Pythia-filtered events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.000629459	0	0
pt=3-4	0.000416493	0	0
pt=4-6	0.000517014	0	0
pt=6-9	0.00037183	0	0
pt=9-15	0.000287305	0	0
pt=15-25	0.000350401	0	0
pt=25-35	0.000520524	0	0

2010.04.17 BFC and Pythia QA: 10% gain shift: lowered thresholds

See this post from Alice for QCD sample rates with slightly lower thresholds

QCD and gamma-jet data samples are described here (filter parameters are listed below)

Pythia filter configuration

StEemcGammaFilter:: running the TEST mode (accepting all events). Set mFilterMode=1 to actually reject events
StEemcGammaFilter:: mConeRadius 0.22 mSeedThreshold 2.4 mClusterThreshold 3.3 mEtaLow 0.95 mEtaHigh 2.1 mMaxVertex 120
StEemcGammaFilter:: mCalDepth 279.5 mMinPartEnergy 1e-05 mHadronScale 1 mFilterMode 0 mPrintLevel 1

BFC filter configuration

StChain:INFO - Init() : Using gamma filter on the EEMC
StChain:INFO - Init() : EEMC Sampling Fraction = 0.05
StChain:INFO - Init() : Seed energy threshold = 2.8 GeV
StChain:INFO - Init() : Cluster eT threshold = 3.8 GeV
StChain:INFO - Init() : Maximum vertex = +/- 120 cm
StChain:INFO - Init() : Running the TEST mode (accepting all events). Set mFilterMode=1 to actually reject events in BFC

StEEmcSlowMaker

BFC:INFO - setTowerGainSpread(): gain spread: 0; gain mean value: 1.1

Accept/Reject relative to the total number of Pythia generated events

Figure 1: Fraction of accepted events

Accept rate: fract. of generated events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.01732	0.1343	0.00628
pt=3-4	0.05818	0.3001	0.0261
pt=4-6	0.1317	0.46492	0.07864
pt=6-9	0.17804	0.56314	0.15092
pt=9-15	0.17226	0.57516	0.15964
pt=15-25	0.13356	0.46894	0.1179
pt=25-35	0.062	0.28546	0.05482

Figure 2: False rejection (Y-axis scale is 10^-3)

False reject: fract. of generated events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	2e-05	2e-05	0
pt=3-4	6e-05	6e-05	0
pt=4-6	6e-05	4e-05	0
pt=6-9	0.0001	6e-05	0
pt=9-15	2e-05	2e-05	0
pt=15-25	2e-05	0	0
pt=25-35	2e-05	2e-05	0

Accept/Reject relative to the number of Pythia filter accepted events

Figure 3: Fraction of accepted events relative to Pythia filter accepted events

Accept rate: fract. of Pythia-filtered events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.128816	1	0.0415488
pt=3-4	0.193735	1	0.0808397
pt=4-6	0.283232	1	0.161877
pt=6-9	0.316049	1	0.260291
pt=9-15	0.29943	1	0.26876
pt=15-25	0.284813	1	0.240415
pt=25-35	0.217053	1	0.175156

Figure 4: False rejection relative to Pythia filter accepted events

False reject: fract. of Pythia-filtered events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0	0	0
pt=3-4	0	0	0
pt=4-6	4.30182e-05	0	0
pt=6-9	7.10303e-05	0	0
pt=9-15	0	0	0
pt=15-25	4.26494e-05	0	0
pt=25-35	0	0	0

2010.04.17 Pythia/BFC gamma-filter accaptance vs. gamma candidate pt, energy, and eta

Data sample used:
Pythia prompt photon Monte-Carlo (partonic pt bins are combined without weights)

Common event cuts:
reconstruct at least one gamma candidate, |v_z| <120, !=0 l2e-gamma-trigger=fired

Figure 1:

upper panel:
Number of candidates vs. gamma candidate pt for specific filter/trigger fired.
lower panel:
Pythia/bfc filter false rejection vs. gamma candidate pt
left panel:
Pythis (bfc) filter configured with seed=2.6 (3.4) and cluster=3.6 (4.5)
right panel:
Pythis (bfc) filter configured with seed=2.4 (2.8) and cluster=3.3 (3.8)

Figure 2: Same as Fig. 1 vs. gamma candidate energy

Figure 3: Same as Fig. 1 vs. gamma candidate pseudo-rapidity

2010.04.30 BFC and Pythia QA after gamma-maker 3x3 cluser fix

Related inks:

3x3 clusering and gamma maker fix
Alice' study with QCD sample
QCD and gamma-jet data samples are described here
(filter parameters are listed below)

Number of generated events per partnic pt bin:
gamma-jet: 25K
QCD(2-4): 50K
QCD(4-55): 25K

Pythia filter configuration

StEemcGammaFilter:: running the TEST mode (accepting all events). Set mFilterMode=1 to actually reject events
StEemcGammaFilter:: mConeRadius 0.22 mSeedThreshold 3.8 mClusterThreshold 5 mEtaLow 0.95 mEtaHigh 2.1 mMaxVertex 120
StEemcGammaFilter:: mCalDepth 279.5 mMinPartEnergy 1e-05 mHadronScale 1 mFilterMode 0 mPrintLevel 1

BFC filter configuration

StChain:INFO - Init() : Using gamma filter on the EEMC
StChain:INFO - Init() : EEMC Sampling Fraction = 0.05
StChain:INFO - Init() : Seed energy threshold = 3.8 GeV
StChain:INFO - Init() : Cluster eT threshold = 5 GeV
StChain:INFO - Init() : Maximum vertex = +/- 120 cm
StChain:INFO - Init() : Running the TEST mode (accepting all events). Set mFilterMode=1 to actually reject events in BFC

StEEmcSlowMaker configuration

BFC:INFO - setTowerGainSpread(): gain spread: 0; gain mean value: 1.1

GammaMaker configuration

runSimuGammaTreeMaker():: GammaMaker config: ConeRadius 0.7 ClusterEtThreshold 5.5 SeedEnergyThreshold 4.2 ClusterEnergyThreshold 5.5 BsmdRange 0.05237 EsmdR ange 20

A2Emaker configuration

StEEmcA2EMaker *EEanalysis = new StEEmcA2EMaker("mEEanalysis");
EEanalysis->threshold(3.0, 0);      // tower threshold (ped+N sigma)
EEanalysis->threshold(3.0, 1);      // pre1 threshold
EEanalysis->threshold(3.0, 2);      // pre2 threshold
EEanalysis->threshold(3.0, 3);      // post threshold
EEanalysis->threshold(3.0, 4);      // smdu threshold
EEanalysis->threshold(3.0, 5);      // smdv threshold

Trigger configuration

emulate L2E-gamma trigger for Run 2006 [eemc-http-mb-l2gamma:: id 137641]
Trigger conditions:
cluster Et (2x2) = 5.2GeV
seed Et = 3.7GeV

Accept/Reject relative to the total number of Pythia generated events

Figure 1: Fraction of accepted events

Accept rate: fract. of generated events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.00288	0.01472	0.00616
pt=3-4	0.01504	0.06192	0.02576
pt=4-6	0.06824	0.22112	0.07548
pt=6-9	0.15848	0.45836	0.15016
pt=9-15	0.1584	0.50416	0.15812
pt=15-25	0.12112	0.42076	0.11916
pt=25-55	0.05356	0.2292	0.0538
QCD
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	2e-05	0.00258	6e-05
pt=3-4	6e-05	0.00854	0.00022
pt=4-6	0.00072	0.03492	0.00076
pt=6-9	0.00564	0.144	0.00496
pt=9-15	0.0242	0.36036	0.0186
pt=15-25	0.07368	0.47592	0.05008
pt=25-55	0.0684553	0.323374	0.0557724

Figure 2: False rejection (Y-axis scale is 10^-3)

False reject: fract. of generated events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.0002	0.00012	0
pt=3-4	0.0002	0.00056	0
pt=4-6	0.00096	0.00132	0
pt=6-9	0.0006	0.00032	0
pt=9-15	0.0002	4e-05	0
pt=15-25	4e-05	0	0
pt=25-55	8e-05	8e-05	0
QCD
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0	0	0
pt=3-4	0	0	0
pt=4-6	0	0	0
pt=6-9	0.00012	0	0
pt=9-15	8e-05	8e-05	0
pt=15-25	0.00016	4e-05	0
pt=25-55	0.000203252	0	0

Accept/Reject relative to the number of Pythia filter accepted events

Figure 3: Fraction of accepted events relative to Pythia filter accepted events

Accept rate: fract. of Pythia-filtered events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.17663	1	0.171196
pt=3-4	0.215762	1	0.197028
pt=4-6	0.291787	1	0.259407
pt=6-9	0.344096	1	0.313814
pt=9-15	0.314107	1	0.301254
pt=15-25	0.28786	1	0.269798
pt=25-55	0.233333	1	0.211693
QCD
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.00775194	1	0
pt=3-4	0.00702576	1	0.00936768
pt=4-6	0.0183276	1	0.0160367
pt=6-9	0.0386111	1	0.0294444
pt=9-15	0.0667111	1	0.048618
pt=15-25	0.154816	1	0.102706
pt=25-55	0.211691	1	0.163545

Figure 4: False rejection relative to Pythia filter accepted events

False reject: fract. of Pythia-filtered events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.0108696	0	0
pt=3-4	0.00258398	0	0
pt=4-6	0.00325615	0	0
pt=6-9	0.00113448	0	0
pt=9-15	0.00031736	0	0
pt=15-25	9.50661e-05	0	0
pt=25-55	0.00017452	0	0
QCD
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0	0	0
pt=3-4	0	0	0
pt=4-6	0	0	0
pt=6-9	0.000833333	0	0
pt=9-15	0.000222	0	0
pt=15-25	0.000252143	0	0
pt=25-55	0.000628536	0	0

Figure 5: False rejection relative to trigger accepted events

False reject: fract. of triggered events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.623377	0.558442	0
pt=3-4	0.517081	0.464286	0
pt=4-6	0.240594	0.192369	0
pt=6-9	0.0348961	0.0167821	0
pt=9-15	0.0111308	0.00151783	0
pt=15-25	0.00872776	0.00134273	0
pt=25-55	0.00966543	0.00148699	0
QCD
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.333333	0.333333	0
pt=3-4	0.363636	0.181818	0
pt=4-6	0.631579	0.263158	0
pt=6-9	0.282258	0.0887097	0
pt=9-15	0.202151	0.0301075	0
pt=15-25	0.0686901	0.000798722	0
pt=25-55	0.0291545	0.000728863	0

05 May

May 2010 posts

2010.05.03 Pythia/BFC gamma-filter accaptance vs. gamma candidate pt (after gamma-maker 3x3 cluser fix)

Related inks:

BFC and Pythia filter biases vs. partonic pt
3x3 clusering and gamma maker fix
Alice' study with QCD sample
QCD and gamma-jet data samples are described here
(filter parameters are listed below)

Number of generated events per partnic pt bin:
gamma-jet: 25K
QCD(2-4): 50K
QCD(4-55): 25K

Pythia filter configuration

BFC filter configuration

StEEmcSlowMaker configuration

BFC:INFO - setTowerGainSpread(): gain spread: 0; gain mean value: 1.1

GammaMaker configuration

runSimuGammaTreeMaker():: GammaMaker config: ConeRadius 0.7 ClusterEtThreshold 5.5 SeedEnergyThreshold 4.2 ClusterEnergyThreshold 5.5 BsmdRange 0.05237 EsmdR ange 20

A2Emaker configuration

StEEmcA2EMaker *EEanalysis = new StEEmcA2EMaker("mEEanalysis");
EEanalysis->threshold(3.0, 0); // tower threshold (ped+N sigma)
EEanalysis->threshold(3.0, 1); // pre1 threshold
EEanalysis->threshold(3.0, 2); // pre2 threshold
EEanalysis->threshold(3.0, 3); // post threshold
EEanalysis->threshold(3.0, 4); // smdu threshold
EEanalysis->threshold(3.0, 5); // smdv threshold

Trigger configuration

emulate L2E-gamma trigger for Run 2006 [eemc-http-mb-l2gamma:: id 137641]
Trigger conditions:
cluster Et (2x2) = 5.2GeV
seed Et = 3.7GeV

Accept/Reject relative to the total number of offline selected events

Definition: offline selected events are events which satisfy to the following conditions:

Online condition (L2E-gamma trigger fired)
Reconstructed vetrex (|v_z|<120cm)
Offline condition (at least one gammaMaker candidate found)

Figure 1a:
(upper plots) Gamma candidate yields vs. candidate pt (all partonic pt bins, no pt weights)
(lower plots) False rejection: histograms in the upper panel scaled by L2E-gamma-trigger yield (blue histogram)

Figure 1b: Same ad Fig. 1a with zoom into low pt region

Yields for each of partonic pt bins separately

Figure 2: Same ad Fig. 1b for partonic pt=2-3

Figure 3: Same ad Fig. 1b for partonic pt=3-4

Figure 4: Same ad Fig. 1b for partonic pt=4-6

Figure 5: Same ad Fig. 1b for partonic pt=6-9

Figure 6: Same ad Fig. 1b for partonic pt=9-15

Figure 7: Same ad Fig. 1b for partonic pt=15-25

Figure 8: Same ad Fig. 1b for partonic pt=25-55

2010.05.05 Starsim/bfc timing tests

Related inks:

Filter's configurations and bias/acceptance tests used for this timing study

Figure 1: BFC filter processing time for accepted events
Note an extra peaks around 23/33/43 seconds for the QCD sample
(they also present but less pronounced in gamma-jet sample)
I found that these are processing times needed for the first accepted by filter events
(not always the time for the first processed event and depends on filter acceptance rate).
Processing is much longer due to time needed to initiaize additional stuff in bfc makers
and it is ignored in the total cpu time estimate

Figure 1b:
1st row: starsim time per partonic pt bin
2nd row: bfc time for accepted events per partonic pt bin
3rd row: bfc time for rejected events per partonic pt bin

Average starsim/bfc timing (ignoring times in Fig. 1 with more that 18 seconds):

Gamma-jets   bfc:acc     starsim    bfc:rej
pt=25-55       6.49765   20.2376    0.139911
pt=15-25      4.04562    11.8268    0.095903
pt=9-15        4.84475    11.4816    0.112344
pt=6-9         6.19909    13.0528   0.143109
pt=4-6         4.86546    9.8856      0.114983
pt=3-4         5.07415    10.404       0.12363
pt=2-3         4.04254    9.04627    0.0995413

QCD bfc:acc       starsim       bfc:rej
pt=25-55   6.18626    14.9944    0.13752
pt=15-25   6.20077    12.9668    0.126722
pt=9-15    6.848         13.7706    0.140625
pt=6-9    5.29513    10.3708    0.110363
pt=4-6    6.29547    11.5318    0.127077
pt=3-4    4.45859    10.1538    0.0986285
pt=2-3    5.26187    13.9151    0.114372

2010.05.13 BFC and Pythia QA after Pythia Eta -> - Eta

Related inks:

Results before bug fix (See Fig. 1 upper right)

Pythia filter bug details:

bug in Pythia Filter StEemcGammaFilter.cxx:

double scale = (mCalDepth-v[2]) / p[2];
for(unsigned int j = 0; j < 3; ++j) p[j] = p[j] * scale + v[j];

Should be "abs(p[2])" in the "scale" factor.
Otherwise this will transfer all - Eta -> Eta,
and such tracks will pass the consequent Endcap rapidity cut:

if(detectorV.Eta() < mEtaLow || detectorV.Eta() > mEtaHigh) continue;

what will increase Pythia filter accept rate by a factor of 2.

Number of generated events per partnic pt bin:

Note: not every jib finished due to RCF scheduler upgrade
gamma-jet: 25K
QCD(2-4): 50K
QCD(4-55): 25K

Pythia filter configuration

BFC filter configuration

StChain:INFO - Init() : Using gamma filter on the EEMC
StChain:INFO - Init() : EEMC Sampling Fraction = 0.05
StChain:INFO - Init() : Seed energy threshold = 3.3 GeV
StChain:INFO - Init() : Cluster eT threshold = 4.5 GeV
StChain:INFO - Init() : Maximum vertex = +/- 120 cm
StChain:INFO - Init() : Running the TEST mode (accepting all events). Set mFilterMode=1 to actually reject events in BFC

StEEmcSlowMaker configuration

BFC:INFO - setTowerGainSpread(): gain spread: 0; gain mean value: 1.1

GammaMaker configuration

runSimuGammaTreeMaker():: GammaMaker config: ConeRadius 0.7 ClusterEtThreshold 5.5 SeedEnergyThreshold 4.2 ClusterEnergyThreshold 5.5 BsmdRange 0.05237 EsmdR ange 20

A2Emaker configuration

Trigger configuration

emulate L2E-gamma trigger for Run 2006 [eemc-http-mb-l2gamma:: id 137641]
Trigger conditions:
cluster Et (2x2) = 5.2GeV
seed Et = 3.7GeV

Accept/Reject relative to the total number of Pythia generated events

Figure 1: Fraction of accepted events

Accept rate: fract. of generated events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.00772455	0.00820359	0.00646707
pt=3-4	0.0276027	0.0311644	0.024726
pt=4-6	0.0924862	0.0980663	0.0743646
pt=6-9	0.165708	0.217554	0.149013
pt=9-15	0.167815	0.258319	0.162353
pt=15-25	0.123	0.215667	0.117083
QCD
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.000107527	0.000430108	0.000107527
pt=3-4	0.000117647	0.00358824	5.88235e-05
pt=4-6	0.00115385	0.0146154	0.000576923
pt=6-9	0.00932927	0.06	0.00530488
pt=9-15	0.0325	0.178846	0.0173077
pt=15-25	0.0860331	0.253802	0.0510331

Figure 2: False rejection

False reject: fract. of generated events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0	0.000359281	0
pt=3-4	0	0.000616438	0
pt=4-6	0.000165746	0.00436464	0
pt=6-9	0.000128755	0.00270386	0
pt=9-15	0	0.000210084	0
pt=15-25	0.00025	0.00025	0
QCD
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0	0	0
pt=3-4	0	0	0
pt=4-6	0	0	0
pt=6-9	0	0.000121951	0
pt=9-15	0	0	0
pt=15-25	4.13223e-05	0.000123967	0

Accept/Reject relative to the number of Pythia filter accepted events

Accept rate: fract. of Pythia-filtered events
GammaJet
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.518248	1	0.49635
pt=3-4	0.505495	1	0.525275
pt=4-6	0.663662	1	0.614648
pt=6-9	0.703492	1	0.64707
pt=9-15	0.645576	1	0.605563
pt=15-25	0.568006	1	0.510433
QCD
pt bin	Bfc Filter	Pythia Filter	L2gamma Trigger
pt=2-3	0.02857	1	0
pt=3-4	0.05161	1	0
pt=4-6	0.0526316	1	0.0394737
pt=6-9	0.126016	1	0.0731707
pt=9-15	0.173118	1	0.0935484
pt=15-25	0.334419	1	0.194074

2010.05.27 Run 6 and Run 9 jet Tree production

Useful inks:

Run 6 Murad's note:
http://www.star.bnl.gov/HyperNews-star/protected/get/jetfinding/664/1/1.html
Run 9 Pibero's note:
http://www.star.bnl.gov/HyperNews-star/protected/get/jetfinding/1010.html
Jan's note on "tower ADC - pedestal > 3 * RMS":
http://www.star.bnl.gov/HyperNews-star/protected/get/jetfinding/1010/2.html
Ily'as post on EEMC only jets for 5-hit branch:
http://www.star.bnl.gov/HyperNews-star/protected/get/jetfinding/1010/3/1/1/1/2.html
Trigger list for Run 9:
http://www.star.bnl.gov/protected/common/common2009/trigger2009/triggers2009.html
Pibero's jet tree format:
http://www4.rcf.bnl.gov/~pibero/spin/jets/2010.04.20
Disk space estimates for Run 9:
http://www.star.bnl.gov/HyperNews-star/protected/get/jetfinding/1010/1/1/2/1.html
http://www.star.bnl.gov/HyperNews-star/protected/get/jetfinding/1010/3/1/1/1.html
http://www.star.bnl.gov/HyperNews-star/protected/get/jetfinding/1010/3/1/1/1/2/1.html

New jet tree format

Description can be found here

Jet finder Run 6 configuration

Jet tree branches

12-point branch
5-point branch (EEMC jets only)

General configuration

cone radius = 0.7
track/tower pT > 0.2 GeV
trigger IDs: See the list below
all primary vertices with rank > 0
hadronic correction: 100% subtraction scheme
track |DCA| < 3 cm
pT-dependent DCAxy cut
track (number of hits) / (number of possible hits) > 0.51
track last point radius > 125 cm (ensure we get at least one point in TPC outer sector)
tower E > 0
tower status = 1
tower ADC - pedestal > 3 * RMS

Triggers

pp@200GeV

addTrigger(127611); //HTTP
137611
5
127821 //HTTP-fast
137821
137822
127212 //HT2
137213
127501 //JP0
137501
127622 //JP0-etot
137622
127221 //JP1
137221
137222
137585 //bemc-jp2
127641 // eemc-http-mb-l2gamma
137641 // eemc-http-mb-l2gamma
6 // eemc-http-mb-l2gamma

Analysis cuts

StppAnaPars* anapars = new StppAnaPars();
anapars->setFlagMin(0);
anapars->setNhits(12);
anapars->setCutPtMin(0.2);
anapars->setAbsEtaMax(2.5);
anapars->setJetPtMin(3.5);
anapars->setJetEtaMax(100.0);
anapars->setJetEtaMin(0);
anapars->setJetNmin(0);

Cone finder configuration

StConePars* cpars = new StConePars();
cpars->setGridSpacing(105, -3.0, 3.0, 120,-TMath::Pi(),TMath::Pi());
cpars->setConeRadius(0.7);
cpars->setSeedEtMin(0.5);
cpars->setAssocEtMin(0.1);
cpars->setSplitFraction(0.5);
cpars->setPerformMinimization(true);
cpars->setAddMidpoints(true);
cpars->setRequireStableMidpoints(true);
cpars->setDoSplitMerge(true);
cpars->setDebug(false);
jetMaker->addAnalyzer(anapars, cpars, bet4pMaker, "ConeJets12");
anapars->setNhits(5);
jetMaker->addAnalyzer(anapars, cpars, bet4pMaker, "ConeJets5");

Disk space required

needs to be updated

Jet finder Run 9 configuration:

Jet tree branches

12-point branch
Tower-only branch (jets without tracking)
5-point branch (EEMC jets only)

General configuration

cone radius = 0.7
track/tower pT > 0.2 GeV
trigger IDs: JP1, L2JetHigh, BBCMB-Cat2 (luminosity monitor)
all primary vertices with rank > 0
hadronic correction: 100% subtraction scheme
track |DCA| < 3 cm
pT-dependent DCAxy cut (a la Run 6 with slight tuning around 0.5 < pT < 1.5 GeV)
track chi^2 < 4
track (number of hits) / (number of possible hits) > 0.51
track last point radius > 125 cm (ensure we get at least one point in TPC outer sector)
tower E > 0
tower status = 1
tower ADC - pedestal > 3 * RMS

Triggers

pp@200GeV

240410 // JP1 // lum: 0.246
240411 // JP1 // lum: 4.045

240650 // L2JetHigh // lum: 3.745
240651 // L2JetHigh // lum: 1.811
240652 // L2JetHigh // lum: 19.769

240620 // L2BGamma // lum: 23.004
240630 // L2EGamma // lum: 3.969
240631 // L2EGamma // lum: 21.592

240013 // BBCMB-Cat2 (luminosity monitor)
240113 // BBCMB-Cat2 (luminosity monitor)
240123 // BBCMB-Cat2 (luminosity monitor)
240223 // BBCMB-Cat2 (luminosity monitor)

pp@500GeV

230410 // JP1 // lum: 0.198
230411 // JP2 // lum: 8.089

230630 // L2EGamma // lum: 3.347

230013 // BBCMB-Cat2 (luminosity monitor)

Analysis cuts

StppAnaPars* anapars = new StppAnaPars;
anapars->setFlagMin(0); // track->flag() > 0
anapars->setCutPtMin(0.2); // track->pt() > 0.2
anapars->setAbsEtaMax(2.5); // abs(track->eta()) < 2.5
anapars->setJetPtMin(5.0);
anapars->setJetEtaMax(100.0);
anapars->setJetEtaMin(0);
anapars->setJetNmin(0);

Cone finder configuration

StConePars* cpars = new StConePars;
cpars->setGridSpacing(105,-3.0,3.0,120,-TMath::Pi(),TMath::Pi());
cpars->setSeedEtMin(0.5);
cpars->setAssocEtMin(0.1);
cpars->setSplitFraction(0.5);
cpars->setPerformMinimization(true);
cpars->setAddMidpoints(true);
cpars->setRequireStableMidpoints(true);
cpars->setDoSplitMerge(true);
cpars->setDebug(false);

Disk space required

1Tb (see links at the top of the page for mode details)

2010.05.28 Photon-jet candidates - Perugia vs. Tune A

Comments:

See no difference in yilds for different tunes for prompt photons.
~ 20% shape differenbce for QCD background Monte-Carlo

Figure 1: Reconstructed gamma-jet candidate yield vs. photon candidate pt in the endcap
(di-jet events found with the jet finder) Prompt photon filtered Monte-Carlo, partonic pt 3-25 GeV
(a)

(b) Same as (a) on a log scale

Figure 2: Same as Fig. 1 for QCD two processes filtered Monte-Carlo, partonic pt 6-9 GeV
(a)

Same on a log scale
(b) Same as (a) on a log scale

Figure 3: QCD ratio Tune-A/Perugia

EEMC simulation spreadsheet: prompt photons and QCD

Same thresholds for Pythia/BFC

1.4M events, 6.8 CPU years, 0.29Tb disk space

parton pt, GeV	Pythia acc	bfc acc wrt. Pythia	Total filter's acc	Sigma, pb	lumi, 1/pb	Number of filtered events to generate	Total CPU time, days	disk space, Gb	starsim CPU, sec	bfc CPU, sec	Total Starsim CPU time, days	Total bfc CPU time, days	bfc acc
g-jets
2-3	0.00820	0.2663	0.00218	1304000	10.0	28491	14.60576	4.45	10.72	4.0	13.270	1.34	0.00288
3-4	0.03116	0.3787	0.01180	293300	10.0	34611	14.85782	5.63	12.19	4.9	12.901	1.96	0.01504
4-6	0.09807	0.5521	0.05414	126300	10.0	68382	20.99942	11.34	12.04	4.7	17.254	3.75	0.06824
6-9	0.21755	0.6688	0.14550	26090	10.0	37961	7.79574	6.48	9.56	3.4	6.280	1.52	0.15848
9-15	0.25832	0.6274	0.16207	4675	10.0	7577	1.95231	1.31	11.56	3.8	1.616	0.34	0.15840
15-25	0.21567	0.5394	0.11633	326	10.0	379	0.13531	0.07	14.34	4.2	0.117	0.02	0.12112
totals:				1754691		177401	60.3	29.29
						0.18	0.16533	years
QCD
2-3	0.00043	0.0185	0.00001	8226000000	0.1	6545	68.81525	1.30	16.62	9.9	68.064	0.75	0.00002
3-4	0.00359	0.0268	0.00010	1295000000	0.2	24907	140.98015	5.32	12.88	8.3	138.594	2.39	0.00006
4-6	0.01462	0.0450	0.00066	440300000	1.0	289582	925.73023	57.50	12.07	7.9	899.343	26.39	0.00072
6-9	0.06000	0.0744	0.00446	57830000	2.0	516306	915.77947	111.13	10.94	6.2	878.663	37.12	0.00564
9-15	0.17885	0.1354	0.02422	7629000	2.0	369484	360.48698	77.18	10.72	5.1	338.666	21.82	0.02420
15-25	0.25380	0.2754	0.06990	381900	1.0	26694	17.99424	5.57	14.43	5.9	16.185	1.81	0.07368
totals:				10027140900		1233518	2429.78632	258.01
						1.23	6.65695	years

	number of events, x 10e6	CPU years	disk space, Gb			total time with 50 CPU, weeks	total time with 100 CPU, weeks
	1.41	6.8	287.3			7.1	3.6

Lowered BFC threshold that Pythia

2M events, 6.9 CPU years, 0.41Tb disk space

parton pt, GeV	Pythia acc	bfc acc wrt. Pythia	Total filter's acc	Sigma, pb	lumi, 1/pb	Number of filtered events to generate	Total CPU time, days	disk space, Gb	starsim CPU, sec	bfc CPU, sec	Total Starsim CPU time, days	Total bfc CPU time, days	bfc acc
g-jets
2-3	0.00820	0.51825	0.00425	1304000	10.0	55439	15.74249	8.66	10.72	3.9	13.270	2.47	0.00772
3-4	0.03116	0.50550	0.01575	293300	10.0	46205	15.47012	7.52	12.19	4.8	12.901	2.57	0.02760
4-6	0.09807	0.66366	0.06508	126300	10.0	82200	21.71983	13.64	12.04	4.7	17.254	4.47	0.09249
6-9	0.21755	0.70349	0.15305	26090	10.0	39930	7.87117	6.82	9.56	3.4	6.280	1.59	0.16571
9-15	0.25832	0.64558	0.16676	4675	10.0	7796	1.96162	1.35	11.56	3.8	1.616	0.35	0.16782
15-25	0.21567	0.56801	0.12250	326	10.0	399	0.13625	0.07	14.34	4.2	0.117	0.02	0.123
totals:				1754691		231970	62.9	38.05
						0.23	0.17233	years
QCD
2-3	0.00043	0.02857	0.00001	8226000000	0.1	10108	68.99113	2.01	16.62	7.9	68.064	0.93	0.00011
3-4	0.00359	0.05161	0.00019	1295000000	0.2	47964	142.14305	10.25	12.88	6.4	138.594	3.55	0.00012
4-6	0.01462	0.05263	0.00077	440300000	1.0	338693	928.66220	67.25	12.07	7.5	899.343	29.32	0.00115
6-9	0.06000	0.12602	0.00756	57830000	2.0	874501	935.22936	188.22	10.94	5.6	878.663	56.57	0.00933
9-15	0.17885	0.17312	0.03096	7629000	2.0	472410	365.62683	98.68	10.72	4.9	338.666	26.96	0.0325
15-25	0.25380	0.33442	0.08488	381900	1.0	32414	18.35070	6.77	14.43	5.8	16.185	2.17	0.08603
totals:				10027140900		1776090	2459.00326	373.18
						1.78	6.73700	years

	number of events, x 10e6	CPU years	disk space, Gb			total time with 50 CPU, weeks	total time with 100 CPU, weeks
	2.01	6.9	411.2			7.2	3.6

(very rough) Run 9 estimates (with 20 sec reject and 30 sec accept bfc time)

1.4M events, 18.3 CPU years, 0.29Tb disk space

parton pt, GeV	Pythia acc	bfc acc wrt. Pythia	Total filter's acc	Sigma, pb	lumi, 1/pb	Number of filtered events to generate	Total CPU time, days	disk space, Gb	starsim CPU, sec	bfc CPU, sec	bfc reject CPU / event	Total Starsim CPU time, days	Total bfc CPU time, days	Bfc accept CPU / event
g-jets
2-3	0.00820	0.2663	0.00218	1304000	10.0	28491	41.33076	4.45	10.72	85.1	20	13.270	28.06	30.00
3-4	0.03116	0.3787	0.01180	293300	10.0	34611	38.06518	5.63	12.19	62.8	20	12.901	25.16	30.00
4-6	0.09807	0.5521	0.05414	126300	10.0	68382	53.83970	11.34	12.04	46.2	20	17.254	36.59	30.00
6-9	0.21755	0.6688	0.14550	26090	10.0	37961	23.81285	6.48	9.56	39.9	20	6.280	17.53	30.00
9-15	0.25832	0.6274	0.16207	4675	10.0	7577	5.28863	1.31	11.56	41.9	20	1.616	3.67	30.00
15-25	0.21567	0.5394	0.11633	326	10.0	379	0.32331	0.07	14.34	47.1	20	0.117	0.21	30.00
totals:				1754691		177401	162.7	29.29
						0.18	0.44565	years
QCD
2-3	0.00043	0.0185	0.00001	8226000000	0.1	6545	150.72171	1.30	16.62	1091.1	20	68.064	82.66	30.00
3-4	0.00359	0.0268	0.00010	1295000000	0.2	24907	356.60524	5.32	12.88	756.3	20	138.594	218.01	30.00
4-6	0.01462	0.0450	0.00066	440300000	1.0	289582	2422.48046	57.50	12.07	454.4	20	899.343	1523.14	30.00
6-9	0.06000	0.0744	0.00446	57830000	2.0	516306	2544.80915	111.13	10.94	278.8	20	878.663	1666.15	30.00
9-15	0.17885	0.1354	0.02422	7629000	2.0	369484	1013.10425	77.18	10.72	157.7	20	338.666	674.44	30.00
15-25	0.25380	0.2754	0.06990	381900	1.0	26694	41.71136	5.57	14.43	82.6	20	16.185	25.53	30.00
totals:				10027140900		1233518	6529.43217	258.01
						1.23	17.88886	years

	number of events, x 10e6	CPU years	disk space, Gb			total time with 50 CPU, weeks	total time with 100 CPU, weeks
	1.41	18.3	287.3			19.1	9.6

06 Jun

June 2010 posts

2010.06.15 First look at data vs. TuneA/Perugia0 filtered MC with latest EEMC geometry

Data samples and colour coding

black: pp2006 data
open green: MC-QCD-TuneA, partonic pt 4-35
solid green: MC-QCD-Perugia0, partonic pt 4-35
(these not shown yet -> still generating data points)
open red MC-prompt-photon-TuneA, partonic pt 3-35
solid red MC-prompt-photon-Perugia0, partonic pt 3-35

Event selection

di-jets from the cone jet-finder algorithm
photon and jet are opposite in phi:
cos (phi_gamma-phi_jet) < -0.8
pt away side jet > 5GeV
detector eta of the away side jet: |eta_jet_det| < 0.8
data: L2e-gamma triggered events
No trigger emulation in Monte-Carlo yet
MC scaled to 3.164^pb based on Pythia luminosity (no fudge factors)

Figure 1: Reconstructed photon candidate pt, pt_gamma (no cut on pt_gamma, pt_jet > 5GeV)

Figure 2: Partonic pt distribution (pt_gamma>7GeV, pt_jet > 5GeV)

Figure 3: Estimate of the contribution from low partonic pt,
only QCD-TuneA MC are shown (pt_gamma>7GeV, pt_jet > 5GeV)
Black line: Exponential fit to partonic pt distribution in 4-7GeV range
                   (pt_gamma>7GeV cut for the photon candidate)
Red line: Exponential fit extrapolated to the partonic pt range below 4GeV.
                Ratio of the area under the red line (integral pt=0-4geV)
                to the area under the green line (integral pt=4-35GeV) is 0.0028.

Comments

(based on Fig. 3)
I would propose we drop both of the lowest parton pt bins,
i.e. pt=2-3 and pt=3-4 (Inherited error for pt_gamma>7GeV < 0.3%)
and instead use our CPU time to produce more
statistics in the 4-35 partonic pt range.
(based on Fig. 2)
There is a small difference between CDF-Tune-A and
Perugia0 tunes partonic pt distributions
even for the prompt photon Monte-Carlo.
Comparison with Perugia0 QCD MC is coming.
Hopefully after that we will be able to decide what
Pythia tune is better match the L2e-gamma data.

2010.06.17 Pythia TuneA/Perugia0 filtered MC vs. pp2006 data

Data samples and colour coding

black circles: pp2006 data
open green: MC-QCD-TuneA, partonic pt 4-35
solid green: MC-QCD-Perugia0, partonic pt 4-35
open red MC-prompt-photon-TuneA, partonic pt 3-35
solid red MC-prompt-photon-Perugia0, partonic pt 3-35

Event selection

di-jets from the cone jet-finder algorithm
photon and jet are opposite in phi:
cos (phi_gamma-phi_jet) < -0.8
pt away side jet > 5GeV
detector eta of the away side jet: |eta_jet_det| < 0.8
data: L2e-gamma triggered events
No trigger emulation in Monte-Carlo yet
MC scaled to 3.164^pb based on Pythia luminosity (no fudge factors)

Plots before cuts on photon candidate pt

Figure 1: Reconstructed photon candidate pt, pt_gamma (no cut on pt_gamma, pt_jet > 5GeV)

Figure 2: Partonic pt distribution (no cut on pt_gamma, pt_jet > 5GeV)

Plots with pt_gamma>7GeV cut

Figure 3: Partonic pt distribution (pt_gamma>7GeV, pt_jet > 5GeV)

Figure 4: Away side jet pt (pt_gamma>7GeV, pt_jet > 5GeV)

Figure 5: Reconstructed z vertex (pt_gamma>7GeV, pt_jet > 5GeV)

Figure 6: Partonic pt distribution for Pythia CDF-Tune-A QCD simulations (pt_gamma>7GeV, pt_jet > 5GeV)

Estimate of the contribution from low partonic pt:
Black line: Exponential fit to partonic pt distribution in 4-7GeV range
Red line: Exponential fit extrapolated to the partonic pt range below 4GeV.
Ratio of the area under the red line (integral over pt=0-4GeV)
to the area under the green line (integral over pt=4-35GeV) is 0.0028 (<0.3%)

Comments

Simulations with Perugia0 tune has a higher yield than that from CDF-Tune-A simulations
Shapes vs. partonic pt are different for Perugia0 and CDF-TuneA simulations
Shapes vs. reconstructed variables are similar for Perugia0 and CDF-TuneA simulations
(based on Fig. 6) I would propose we drop both of the lowest parton pt bins,
i.e. pt=2-3 and pt=3-4 (Inherited error for pt_gamma>7GeV < 0.3%)
and instead use CPU time to produce more statistics in the 4-35 partonic pt range.
More discussion at phana hyper news:
http://www.star.bnl.gov/HyperNews-star/protected/get/phana/496.html

Additional figures

Figure 7a: Photon candidate yield vs. rapidity (pt_gamma>7GeV, pt_jet > 5GeV)
Left: pt_gamma>7GeV; right: zoom into eta < 1 region

Figure 7b: yield vs. jet1 momentum (pt_gamma>7GeV, pt_jet > 5GeV)
Figure 7c: eta yield without pt_gamma cut
Yields ratio for eta <0.95 to the total yield is ~ 1.7% (1004/58766 = 0.0171)

Figure 8: Photon candidate yield vs. rapidity (pt_gamma>7GeV, pt_jet > 5GeV)

Note: trigger condition is not applied in simulations yet
but at high pt the data to Pythia CDF-Tune-A ratio is about 1.28 (at 9GeV: 3200/2500),
what is consistent with an additional 25% scaling factor
used for CIPANP 2009 presentation (see slide 6)

2010.06.18 L2e-gamma trigger effect: Py-CDF-Tune-A, Py-Perugia0, and pp2006 data comparison

Pythia and BFC filter QA vs. gamma candidate pt and eta
L2e-gamma trigger effect with old Monte-Carlo (before EEMC geometry fix)

Data samples and colour coding

black circles: pp2006 data
open green: MC-QCD-TuneA, partonic pt 4-35
solid green: MC-QCD-Perugia0, partonic pt 4-35
open red MC-prompt-photon-TuneA, partonic pt 3-35
solid red MC-prompt-photon-Perugia0, partonic pt 3-35

Event selection

di-jets from the cone jet-finder algorithm
photon and jet are opposite in phi:
cos (phi_gamma-phi_jet) < -0.8
pt away side jet > 5GeV
detector eta of the away side jet: |eta_jet_det| < 0.8
data: L2e-gamma triggered events
No trigger emulation in Monte-Carlo yet
MC scaled to 3.164^pb based on Pythia luminosity (no fudge factors)

Figure 1: Reconstructed photon candidate pt, pt_gamma (no cut on pt_gamma, pt_jet > 5GeV)

Figure 2: Same as Fig. 1 with L2e-gamma condition simulated in Monte-Carlo

Figure 3: Same as Fig. 1, added distribution for photon pt from geant record (prompt photon MC only)

Figure 4: raw jet pt from jet trees: QCD pt=6-9
upper plot: mit0043 M. Betancourt simulations (MIT Simulation Productions)
bottom plot: new filtered MC

2010.06.28 Tests of L2e-gamma trigger emulation with single photon Monte-Carlo

Monte-Carlo configuration

Single photon in the EEMC (flat in eta, pt, phi)
Narrow vertex distribution with sigma=1cm
10 muons thrown in Barrel (|eta|<0.5) to reconstruct vertex
3 muons thrown in each BBC (|eta|~4) to fire the trigger
Run 6 L2e-gamma-trigger id = 137641
STAR geometry tag: y2006h
Photon cuts:
1.1 < eta < 1.95
3 < pt < 15 GeV
0 < phi < 2pi

Trigger effect vs. thrown photon pt, eta, and energy

Figure 1:
Yields vs. thrown photon pt
left: Yields with (red) and without (black) L2e-gamma trigger condition
right: Yield ratio (with/without trigger)

Figure 2: Same as Fig. 1 vs. thrown eta

Figure 3: Same as Fig. 1 vs. thrown energy

Trigger effect vs. reconstructed energy in the EEMC (high tower, 2x1, 3x3, energy and total E_T)

Figure 4: Same as Fig. 1 vs. total reconstructed transverse energy

Figure 5: Same as Fig. 1 vs. reconstructed high tower energy

Figure 6: Same as Fig. 1 vs. reconstructed energy of the 2x1 tower cluster

Figure 7: Same as Fig. 1 vs. reconstructed energy of the 3x3 tower cluster

2010.06.30 Py-tunes (GEANT+L2e-gamma trigger) vs. Run 6 data

Data samples and colour coding

black circles: pp2006 data
open green: MC-QCD-TuneA, partonic pt 4-35
solid green: MC-QCD-Perugia0, partonic pt 4-35
open red MC-prompt-photon-TuneA, partonic pt 3-35
solid red MC-prompt-photon-Perugia0, partonic pt 3-35

Event selection

di-jets from the cone jet-finder algorithm
photon and jet are opposite in phi:
cos (phi_gamma-phi_jet) < -0.8
pt away side jet > 5GeV
detector eta of the away side jet: |eta_jet_det| < 0.8
data : L2e-gamma triggered events
Monte-Carlo: emulated L2e-gamma triggered condition
MC scaled to 3.164^pb based on Pythia luminosity (no fudge factors)

Figure 1: Reconstructed photon candidate pt (no pt_gamma cut, pt_jet > 5GeV)
L2e-gamma condition simulated in Monte-Carlo

Figure 2: Yield ratios (no pt_gamma cut, pt_jet > 5GeV)
Black: data[pp2006] / QCD[Perigia0]
Green: QCD[Perigia0] / QCD[CDF-Tune-A]
Red: g-jet[Perigia0] / g-jet[CDF-Tune-A]

Figure 3: Vertex z distribution (pt_gamma>7GeV, pt_jet > 5GeV)

Figure 4: Simulation yield vs. partonic pt (no pt_gamma cut, pt_jet > 5GeV)

07 Jul

July 2010 posts

2010.07.02 Tests of L2e-gamma trigger emulation with full Pythia+Geant Monte-Carlo

Event selection

di-jets from the cone jet-finder algorithm
photon and jet are opposite in phi:
cos (phi_gamma-phi_jet) < -0.8
pt away side jet > 5GeV
detector eta of the away side jet: |eta_jet_det| < 0.8
data : L2e-gamma triggered events
Run 6 L2e-gamma trigger algo: E_T[2x2] > 5.2, E_T[high tower]>3.7
Monte-Carlo: emulated L2e-gamma triggered condition
MC scaled to 3.164^pb based on Pythia luminosity (no fudge factors)

Plots for the ratio of N[passed L2] to N[before trigger]

Figure 1: Trigger effect vs. reconstructed photon candidate pt (3x3 patch) (no pt_gamma cut, pt_jet > 5GeV)
Dashed lines: Pythia Tune A, solid lines Pythia Perugia0 tune

Figure 2: Trigger effect vs. simulated direct photon pt (no pt_gamma cut, pt_jet > 5GeV)

Figure 3: Trigger effect vs. simulated direct photon eta (no pt_gamma cut, pt_jet > 5GeV)

Figure 4: Trigger effect vs. reconstructed vertex z (no pt_gamma cut, pt_jet > 5GeV)

Figure 5: Trigger effect vs. reconstructed vertex z (with additional pt_gamma >7GeV, pt_jet > 5GeV)

Figure 6: Trigger effect vs. 2x2 cluster Et (no pt_gamma cut, pt_jet > 5GeV)

Figure 7: Trigger effect vs. 1x1 cluster (high tower) Et (no pt_gamma cut, pt_jet > 5GeV)

2010.07.09 Table of L0-BBC, L0-EEMC, and L2Egamma triggers biases

Real and simulated trigger decisions:

BBC stands for emulated L0 BBC trigger condition
EEMC stands for emulated Run 6 L0 EEMC (137832) trigger condition
L2 stands for emulated L2E-gamma (137641) trigger condition
Trig event satisfied to all three simulated trigger conditions: BBC+EEMC+L2
data-EEMC stands for real data L0 EEMC (137832) trigger condition
(available only for fast offline data, not filled in yet)
data-L2 stands for real data L2E-gamma (137641) trigger condition

Data samples:

pp2006 data (3.4K events from st_physics production)
Pythia prompt photon simulations (147 events gamma-filtered for partonic pt=3-25GeV)
Pythia QCD 2->2 simulations (45 events gamma-filtered for partonic pt=6-9GeV)

Notations in the table:

XXX=0 - stands for XXX trigger did not fired
XXX=1 - stands for XXX trigger did fired
XXX=0 YYY=1 stands for XXX trigger did not fired, but YYY did fired


sample	Total	BBC=1	EEMC=1	L2=1	L2=1 EEMC=0	L2=1 BBC=0	L2=0 BBC=1	L2=0 EEMC=1

pp2006, st_physics
(counts)	3396	3306	568	549	5	2	2759	24
(%)	1.0000	0.9735	0.1673	0.1617	0.0015	0.0006	0.8124	0.0071

gamma-jets (3-25GeV)
(counts)	147	119	127	122	3	24	21	8
(%)	1	0.80952	0.86395	0.82993	0.020	0.16327	0.14286	0.054

QCD (6-9GeV)
(counts)	45	38	19	19	1	4	23	1
(%)	1	0.84444	0.42222	0.42222	0.022	0.08889	0.51111	0.022


simu vs. real triggers	data-EEMC=1	data-L2=1	L2=1 data-L2=0	L2=0 data-L2=1	Trig=1 data-L2=0	Trig=0 data-L2=1	EEMC=0 data-EEMC=1	EEMC=1 data-EEMC=0
	572	548	7	6	0	6	0	4
	0.1673	0.1617	0.0021	0.0018	0.0000	0.0018	0.0000	0.0012

2010.07.14 Pythia/BFC gamma-filter bias tests (vs. gamma pt, eta, energy, and phi)

Related inks:

Tests of L2e-gamma trigger emulation with full Pythia+Geant Monte-Carlo
Previous study before filter emulator fixes
QCD and gamma-jet data samples are described here
(filter parameters are listed below)

Number of generated events per partnic pt bin (pt binsa are: 2-3, 3-4, 4-6, 6-9, 9-15, 15-35):
gamma-jets (2-4): 25K/bin
gamma-jets (4-35): 12.5K/bin
QCD(2-4): 50K/bin
QCD(4-35): 25K/bin

Pythia filter configuration

BFC filter configuration

StEEmcSlowMaker configuration

BFC:INFO - setTowerGainSpread(): gain spread: 0; gain mean value: 1 (Fig. 1 only)
BFC:INFO - setTowerGainSpread(): gain spread: 0; gain mean value: 1.1 (Fig. 2 and below)

GammaMaker configuration

runSimuGammaTreeMaker():: GammaMaker config: ConeRadius 0.7 ClusterEtThreshold 5.5 SeedEnergyThreshold 4.2 ClusterEnergyThreshold 5.5 BsmdRange 0.05237 EsmdR ange 20

A2Emaker configuration

StEEmcA2EMaker *EEanalysis = new StEEmcA2EMaker("mEEanalysis");
EEanalysis->threshold(3.0, 0); // tower threshold (ped+N sigma)
EEanalysis->threshold(3.0, 1); // pre1 threshold
EEanalysis->threshold(3.0, 2); // pre2 threshold
EEanalysis->threshold(3.0, 3); // post threshold
EEanalysis->threshold(3.0, 4); // smdu threshold
EEanalysis->threshold(3.0, 5); // smdv threshold

Trigger configuration

(Includes all recent fixes to trigger emulator configuration/software)
emulated L2E-gamma trigger for Run 2006 [eemc-http-mb-l2gamma:: id 137641]
Trigger conditions:
cluster Et (3x3) = 5.2GeV
seed Et = 3.7GeV

Accept/Reject relative to the total number of offline selected events

Definition: offline selected events are events which satisfy to the following conditions:

Online condition (L2E-gamma trigger fired)
Reconstructed vetrex (|v_z|<120cm)
Offline condition (at least one gammaMaker candidate found)

Figure 1:
(upper plots) Gamma candidate yields vs. candidate pt (all partonic pt bins, no pt weights)
(lower plots) False rejection [histograms in the upper panel scaled by L2E-gamma-trigger yield (shown in blue)]
No gain shifts in StEEmcSlowMaker
Note: statistics without gain shifts is smaller because ~10-20% jobs died at RCF

Figure 2: Same as Fig. 1, but with +10% (1.1) gain shifts in StEEmcSlowMaker.

Figure 3: Same as Fig. 2 vs. candidate eta (with +10% (1.1) gain shifts in StEEmcSlowMaker).

Figure 4: Same as Fig. 2 vs. candidate azimuthal angle (with +10% (1.1) gain shifts in StEEmcSlowMaker).

Figure 5: Same as Fig. 2 vs. energy (with +10% (1.1) gain shifts in StEEmcSlowMaker).

2010.07.16 Pythia/BFC gamma-filter bias tests with realistic gain variation

Related inks:

Previous study with ideal gains and gain shift only of +10%
Tests of L2e-gamma trigger emulation with full Pythia+Geant Monte-Carlo
Previous study before filter emulator fixes
QCD and gamma-jet data samples are described here
(filter parameters are listed below)

Number of generated events per partnic pt bin (pt binsa are: 2-3, 3-4, 4-6, 6-9, 9-15, 15-35):
gamma-jets (2-4): 25K/bin
gamma-jets (4-35): 12.5K/bin
QCD(2-4): 50K/bin
QCD(4-35): 25K/bin

Pythia filter configuration

BFC filter configuration

StEEmcSlowMaker configuration with realistic gain shift/smearing

Figure 1: Error for gains from MIP study minus ideal
Data digitized from Scott's presentation at 2008 Calibartion workshop

BFC:INFO - setTowerGainSpread(): gain spread: 0.1; gain mean value: 1.05 (Fig. 1,3 only)
BFC:INFO - setTowerGainSpread(): gain spread: 0.1; gain mean value: 0.95 (Fig. 2,4 and below)

GammaMaker configuration

runSimuGammaTreeMaker():: GammaMaker config: ConeRadius 0.7 ClusterEtThreshold 5.5 SeedEnergyThreshold 4.2 ClusterEnergyThreshold 5.5 BsmdRange 0.05237 EsmdR ange 20

A2Emaker configuration

StEEmcA2EMaker *EEanalysis = new StEEmcA2EMaker("mEEanalysis");
EEanalysis->threshold(3.0, 0); // tower threshold (ped+N sigma)
EEanalysis->threshold(3.0, 1); // pre1 threshold
EEanalysis->threshold(3.0, 2); // pre2 threshold
EEanalysis->threshold(3.0, 3); // post threshold
EEanalysis->threshold(3.0, 4); // smdu threshold
EEanalysis->threshold(3.0, 5); // smdv threshold

Trigger configuration

Accept/Reject relative to the total number of offline selected events

Definition: offline selected events are events which satisfy to the following conditions:

Online condition (L2E-gamma trigger fired)
Reconstructed vetrex (|v_z|<120cm)
Offline condition (at least one gammaMaker candidate found)

Figure 2:
(upper plots) Gamma candidate yields vs. candidate pt (all partonic pt bins, no pt weights)
(lower plots) False rejection [histograms in the upper panel scaled by L2E-gamma-trigger yield (shown in blue)]
StEEmcSlowMaker configured with +5% (scale factor=1.05) gain shifts and 0.1 sigma
Previous plots: 125K events per pt-bin, 250K/pt-bin
(figure below combines previous statisitcs + 18K for partonic pt=6-9 and pt=9-15 GeV bins)

Figure 2b: Filter bias per partonic pt bin (QCD simulations only)

Figure 3: Same as Fig. 1 with gain shift=0.95 and sigma=0.1

Figure 4: Same as Fig. 1 vs. candidate eta with gain shift=1.05 and sigma=0.1

Figure 5: Same as Fig. 1 vs. candidate eta with gain shift=0.95, sigma=0.1

2010.07.20 EEMC simulation spreadsheet: prompt photons and QCD (Updated)

Simulation request spreadsheet (QCD@L=2/pb, photons@L=10/pb)

parton pt, GeV	Pythia acc	bfc acc wrt. Pythia	Total filter's acc	Sigma, pb	lumi, 1/pb	Number of filtered events to generate	Total CPU time, days	disk space, Gb	Number of Pythia filtered events
g-jets
2-3	0.00870	0.2663	0.00232	1280000	10.0	29659	18.75	4.63	111360
3-4	0.03300	0.3787	0.01250	290000	10.0	36237	15.38	5.90	95700
4-6	0.10920	0.5521	0.06029	126700	10.0	76387	25.66	12.67	138356
6-9	0.22320	0.6688	0.14928	26860	10.0	40096	10.74	6.85	59952
9-15	0.25360	0.6274	0.15911	4636	10.0	7376	2.17	1.28	11757
15-35	0.21360	0.5394	0.11522	347	10.0	399	0.17	0.07	740
totals:				1728543		190154	72.9	31.40	417865
						0.19	0.2	years
QCD
2-3	0.00067	0.0185	0.00001	8089000000	0.0		0	0.00
3-4	0.00298	0.0268	0.00008	1302000000	0.0		0	0.00
4-6	0.01312	0.0240	0.00031	413600000	2.0	260469	1428.35	51.72	10852864
6-9	0.06140	0.0640	0.00393	60620000	2.0	476425	1023.1	102.54	7444136
9-15	0.17692	0.1120	0.01982	7733000	2.0	306459	440.74	64.02	2736245
15-35	0.25480	0.2260	0.05758	404300	2.0	46563	34.53	9.72	206031
totals:				9873357300		1089916	2926.72	228.00	21239276
						1.09	8.02	years

QCD lumi, 1/pb	number of events, x 10e6	CPU years	disk space, Gb			total time with 50 CPU, weeks	total time with 100 CPU, weeks
1	.74	4.2	145.4			4.4	2.2
2	1.28	8.2	259.4			8.6	4.3

Timing tests

Figure 1: Timing tests for BFC and Pythia gamma-filters (in seconds)

2010.07.22 Run 6 EEMC gamma-filtered simulation request

Submitted run-6 photon-jet simulation request for spin physics

Request last updated on Aug 19, 2010

Run 6 EEMC gamma-filtered simulation request summary

Total resources estimate for QCD with 1/pb and prompt-photon with 10/pb suimulations:

CPU: 4.2 CPU years (2.2 weeks of running on a 100 CPUs)
Disk space: 0.15Tb
Numbe of filtered events: 0.74M

partonic pt	QCD		prompt photon
	total	Pythia	total	Pythia
2-3	0	0	30K	110K
3-4	0	0	36K	95K
4-6	130K	5.5M	76K	140K
6-9	240K	3.7M	40K	60K
9-15	150K	1.4M	10K	12K
15-35	23K	100K	1K	3K

Latest filter bias/timing test and simulation request spreasheet

Note: These and all other studies are linked from here

Filter code in cvs

Pythia gamma-filter code:
StEemcGammaFilter.cxx
StEemcGammaFilter.h
BFC gamma-filter code:
StEemcGammaFilterMaker.cxx
StEemcGammaFilterMaker.h
eemcGammaFilterMakerParams.idl

Run 6 GMT timestamps

See this study for more details and plots

Request an equal fraction (10%) for each of the 10 timestamps below:
sdt20060516.152000 (GMT during run 7136022)
sdt20060518.073700 (GMT during run 7138010)
sdt20060520.142000 (GMT during run 7140024)
sdt20060521.052000 (GMT during run 7141011)
sdt20060522.124500 (GMT during run 7142029)
sdt20060523.204400 (GMT during run 7143044)
sdt20060525.114000 (GMT during run 7145023)
sdt20060526.114000 (GMT during run 7146020)
sdt20060528.144500 (GMT during run 7148028)
sdt20060602.071500 (GMT during run 7153015)

------------------------ REQUEST DETAILS BELOW ----------------------------------------

prompt photons and QCD simulations

Request TypeEvent generator simulation, with filtering

General Information


Request ID
Priority: EC	0
Priority: pwg	High
Status	New
Physics Working Group	Spin
Requested by	Photon group for SPIN PWG
Contact email(s)	ilya.selyuzhenkov@gmail.com, bridgeman@hep.anl.gov
Contact phone(s)
PWG email(s)	starspin-hn@www.star.bnl.gov
Assigned Deputy:	Not assigned
Assigned Helper:	Not assigned

Description

Endcap photon-jets request

Global Simulation Settings


Request type:	Event generator simulation, with filtering
Number of events	See list for each partonic pt bins
Magnetic Field	Full-Field
Collision Type	pp@200GeV
Centrality	---- SELECT CENTRALITY ----
BFC tags	trs fss y2006h Idst IAna l0 tpcI fcf ftpc Tree logger ITTF Sti VFPPV bbcSim tofsim tags emcY2 EEfs evout -dstout IdTruth geantout big fzin MiniMcMk eemcDb beamLine clearmem
Production	---- SELECT PRODUCTION TAG ----
Geometry: simu	y2006h
Geometry: reco	y2006h
Library	use library with approved filter code checked in
Vertex option	Leave vertex to be reconstructed vertex, and use VFPPVnoCTB with beamline
Pileup option	No
Detector Set	TPC, ETOW, BTOW, BSMD, ESMD, BPRS, EPRE1, EPRE2, EPOST, TOF, BBC, SVT, SSD

Data Sources

MC Event Generator


Event generator	Pythia
Extra options	Additional libraries required for Eemc-gamma Pythia-level filter gexec $ROOTSYS/lib/libCint.so gexec $ROOTSYS/lib/libCore.so gexec $ROOTSYS/lib/libMathCore.so gexec $ROOTSYS/lib/libMatrix.so gexec $ROOTSYS/lib/libPhysics.so gexec .sl53_gcc432/lib/StMCFilter.so // filter library Prompt photon Pythia processes: MSUB (14)=1 MSUB (18)=1 MSUB (29)=1 MSUB (114)=1 MSUB (115)=1 QCD 2->2 Pythia processes: MSUB (11) = 1 MSUB (12) = 1 MSUB (13) = 1 MSUB (28) = 1 MSUB (53) = 1 MSUB (68) = 1 Pro-pT0 Pythia tune: call pytune(329)
Vertex Z, cm	-120 < Vertex < 120
Gaussian sigma in X,Y,Z if applicable	0, 0, 55 200 GeV
Vertex offset: x, mm	0.0cm
Vertex offset: y, mm	-0.3cm
Φ (phi), radian	0 < Φ < 6.29
η (eta)	Default (include Barrel, Endcap, BBC)
Pt bin, GeV	See list above for QCD and g-jet samples
Macro file	Pythia gamma-filter code: StEemcGammaFilter.cxx StEemcGammaFilter.h BFC gamma-filter code: StEemcGammaFilterMaker.cxx StEemcGammaFilterMaker.h eemcGammaFilterMakerParams.idl Private bfc: /star/u/seluzhen/star/spin/MCgammaFilter/scripts/bfc.C

2010.07.23 PyTune comparison with photon candidates: Perugia0 vs. Pro-PT0

Data samples and colour coding

black Pythia QCD Monte-Carlo with Pro-Pt0 tune (pytune=329), partonic pt 9-15
red Pythia QCD Monte-Carlo with Perugia0 tune (pytune=320), partonic pt 9-15

Event selection

Ran full Pythia+GSTAR simulation and require at least one
reconstucred EEMC photon candidate in the gamma Maker.

Figure 1:
Left: Reconstructed photon candidate transverse momentum (no normalization factor applied)
Right: ratio of Pro-Pt0/Perugia0 simulations (solid Line: "a+b*x" fit to ratio)
Event selections: require at least one reconstucred EEMC photon candidate

Figure 2:
Same as in Fig. 1 with different event selection criteria:
L2E-gamma, |v_z| < 120cm, at least one EEMC gamma candidate

Pytune parameters comparison table

pytune(320) Perugia 0
P. Skands, Perugia MPI workshop October 2008
and T. Sjostrand & P. Skands, hep-ph/0408302
CR by M. Sandhoff & P. Skands, in hep-ph/0604120
LEP parameters tuned by Professor

pytune(329) Pro-pT0
See T. Sjostrand & P. Skands, hep-ph/0408302
and M. Sandhoff & P. Skands, in hep-ph/0604120
LEP/Tevatron parameters tuned by Professor

Red text indicates the parameter which are different between tunes

Parameter	Perugia 0	Pro-pT0	Parameter description
MSTP(51)	7	7	PDF set
MSTP(52)	1	1	PDF set internal (=1) or pdflib (=2)
MSTP(64)	3	2	ISR alphaS type
PARP(64)	1.0000	1.3000	ISR renormalization scale prefactor
MSTP(67)	2	2	ISR coherence option for 1st emission
PARP(67)	1.0000	4.0000	ISR Q2max factor
MSTP(68)	3	3	ISR phase space choice & ME corrections (Note: MSTP(68) is not explicitly (re-)set by PYTUNE)
MSTP(70)	2	2	ISR IR regularization scheme
MSTP(72)	1	0	ISR scheme for FSR off ISR
PARP(71)	2.0000	2.0000	FSR Q2max factor for non-s-channel procs
PARJ(81)	0.2570	0.2570	FSR Lambda_QCD scale
PARJ(82)	0.8000	0.8000	FSR IR cutoff
MSTP(81)	21	21	UE model
PARP(82)	2.0000	1.8500	UE IR cutoff at reference ecm (Note: PARP(82) replaces PARP(62).)
PARP(89)	1800.0000	1800.0000	UE IR cutoff reference ecm
PARP(90)	0.2600	0.2200	UE IR cutoff ecm scaling power
MSTP(82)	5	5	UE hadron transverse mass distribution
PARP(83)	1.7000	1.8000	UE mass distribution parameter
MSTP(88)	0	0	BR composite scheme
MSTP(89)	1	1	BR colour scheme
PARP(79)	2.0000	1.1800	BR composite x enhancement
PARP(80)	0.0500	0.0100	BR breakup suppression
MSTP(91)	1	1	BR primordial kT distribution
PARP(91)	2.0000	2.0000	BR primordial kT width <\|kT\|>
PARP(93)	10.0000	7.0000	BR primordial kT UV cutoff
MSTP(95)	6	6	FSI colour (re-)connection model
PARP(78)	0.3300	0.1700	FSI colour reconnection strength
PARP(77)	0.9000	0.0000	FSI colour reco high-pT dampening streng
MSTJ(11)	5	5	HAD choice of fragmentation function(s)
PARJ(21)	0.3130	0.3130	HAD fragmentation pT
PARJ(41)	0.4900	0.4900	HAD string parameter a
PARJ(42)	1.2000	1.2000	HAD string parameter b
PARJ(46)	1.0000	1.0000	HAD Lund(=0)-Bowler(=1) rQ (rc)
PARJ(47)	1.0000	1.0000	HAD Lund(=0)-Bowler(=1) rb

08 Aug

August 2010 posts

2010.08.09 PyTune comparison with gamma candidates from dijets: Perugia0 vs. Pro-PT0

Tunes compared

CDF Tune A
Perugia0
Pro-pT0

Event selection

di-jets from the cone jet-finder algorithm
photon and jet are opposite in phi:
cos (phi_gamma-phi_jet) < -0.8
pt away side jet > 5GeV
detector eta of the away side jet: |eta_jet_det| < 0.8
data : L2e-gamma triggered events
Monte-Carlo: emulated L2e-gamma triggered condition
MC scaled to 3.164^pb based on Pythia luminosity (no fudge factors)

Figure 1a: Reconstructed photon candidate pt (L2e-gamma condition simulated in Monte-Carlo)

Figure 1b: Same as Fig. 1 on a linear scale and zoom into low pt

Figure 2: QCD Monte-Carlo yield to pp2006 data ratio

Figure 3: Prompt photon Monte-Carlo yield to pp2006 data ratio

Figure 4: Simulation yield vs. partonic pt (on a linear scale)

Figure 5: Simulation yield vs. partonic pt (log scale)

2010.08.10 Timestamps study for the simulation request

Summary for the gamma filtered simulation request

Generate Monte-Carlo events for
10 different timestamps and 10% of statistics each:

sdt20060516.152000 (GMT during run 7136022)
sdt20060518.073700 (GMT during run 7138010)
sdt20060520.142000 (GMT durign run 7140024)
sdt20060521.052000 (GMT during run 7141011)
sdt20060522.124500 (GMT during run 7142029)
sdt20060523.204400 (GMT during run 7143044)
sdt20060525.114000 (GMT during run 7145023)
sdt20060526.114000 (GMT during run 7146020)
sdt20060528.144500 (GMT during run 7148028)
sdt20060602.071500 (GMT during run 7153015)

Original list of timestamps

(from http://www.star.bnl.gov/HyperNews-star/protected/get/phana/481.html)

sdt20060512.043500 (GMT during run 7132005)
sdt20060513.064000 (GMT during run 7133011)
sdt20060514.090000 (GMT during run 7134015)
sdt20060516.152000 (GMT during run 7136022)
sdt20060518.073700 (GMT during run 7138010)
sdt20060520.142000 (GMT durign run 7140024)
sdt20060521.052000 (GMT during run 7141011)
sdt20060522.124500 (GMT during run 7142029)
sdt20060523.204400 (GMT during run 7143044)
sdt20060525.114000 (GMT during run 7145023)
sdt20060526.114000 (GMT during run 7146020)
sdt20060528.144500 (GMT during run 7148028)
sdt20060602.071500 (GMT during run 7153015)
sdt20060604.191200 (GMT during run 7155043)

Figure 1: Number of events from Run 6 golden runs
which fired L2e-gamma trigger (trigger id 137641 and 127641)
(using jet trees regenerated in new format by Wayne/Renee)

7132005: 0
7133011: 0
7134015: 10474
7136022: 171217
7138010: 221567
7140024: 62826
7141011: 174207
7142029: 187048
7143044: 189752
7145023: 142799
7146020: 133758
7148028: 181269
7153015: 145129
7155043: 89428

Figure 2: Fraction of events per time stamps

09 Sep

September 2010 posts

2010.09.08 First look at the official EEMC gamma filtered production

Tune comparison with private Monte-Carlo (Tune A vs. Perugia0 vs. Pro-pT0)

Event selection

Official EEMC gamma filtered Monte-Carlo with Pro-pT0 tune
di-jets from the cone jet-finder algorithm
photon and jet are opposite in phi:
cos (phi_gamma-phi_jet) < -0.8
pt away side jet > 5GeV
detector eta of the away side jet: |eta_jet_det| < 0.8
data : L2e-gamma triggered events
Monte-Carlo: emulated L2e-gamma triggered condition
MC scaled to 3.164^pb based on Pythia luminosity (no fudge factors)

Figure 1: Reconstructed photon candidate pt (L2e-gamma condition simulated in Monte-Carlo)

Figure 2: Partonic pt

Figure 3: Thrown photon pt (from Geant record, prompt photon sample only)

2010.09.10 Data to MC comparison with official EEMC gamma filtered production

First look at the official EEMC gamma filtered production

Event selection

Official EEMC gamma filtered Monte-Carlo with Pro-pT0 tune
di-jets from the cone jet-finder algorithm
photon and jet are opposite in phi:
cos (phi_gamma-phi_jet) < -0.8
pt away side jet > 5GeV
detector eta of the away side jet: |eta_jet_det| < 0.8
data : L2e-gamma triggered events
Monte-Carlo: emulated L2e-gamma triggered condition
QCD Monte-Carlo scaled to the yield in the data (MC down scaled by a factor of 1.8)
All plots with gamma pt >7GeV cut

Figure 1: Reconstructed photon candidate pt

Figure 2: Reconstructed away side jet pt

Figure 3: z vertex distribution

Figure 4: 3x3 tower cluster energy

Figure 5: 2x1 tower cluster energy

Figure 6: Reconstructed photon candidate (detector) rapidity

Figure 7: Reconstructed away side jet rapidity

2010.09.13 Data vs. official filtered MC: cluster energy ratios

Event selection

Official EEMC gamma filtered Monte-Carlo with Pro-pT0 tune
di-jets from the cone jet-finder algorithm
photon and jet are opposite in phi:
cos (phi_gamma-phi_jet) < -0.8
pt away side jet > 5GeV
detector eta of the away side jet: |eta_jet_det| < 0.8
data : L2e-gamma triggered events
Monte-Carlo: emulated L2e-gamma triggered condition
QCD Monte-Carlo scaled to the yield in the data (MC down scaled by a factor of 1.8)
All plots with gamma pt >7GeV and jet pt >5GeV cuts

Figure 1: Cluster energy ratio: 2x1/2x2

Figure 2: Cluster energy ratio: 2x1/3x3

Figure 3: Cluster energy ratio: 2x2/3x3

Figure 4: Cluster energy ratio: 2x1/E(R=0.7)

Figure 5: Cluster energy ratio: 2x2/E(R=0.7)

Figure 6: Cluster energy ratio: 3x3/E(R=0.7)

2010.09.15 Data vs. official filtered MC: energy deposition in various EEMC layers

Event selection

Official EEMC gamma filtered Monte-Carlo with Pro-pT0 tune
di-jets from the cone jet-finder algorithm
photon and jet are opposite in phi:
cos (phi_gamma-phi_jet) < -0.8
pt away side jet > 5GeV
detector eta of the away side jet: |eta_jet_det| < 0.8
data : L2e-gamma triggered events
Monte-Carlo: emulated L2e-gamma triggered condition
QCD Monte-Carlo scaled to the yield in the data (MC down scaled by a factor of 1.8)
All plots with gamma pt >7GeV and jet pt >5GeV cuts

Figure 1: 3x3 tower cluster energy

Figure 2: 25 central SMD u-strip energy

Figure 3: 3x3 pre-shower1 cluster energy

Figure 4: 3x3 pre-shower2 cluster energy

Figure 5: 3x3 post-shower cluster energy

2010.09.23 Neutral energy jet shape comparison for various tunes

Data vs. official filtered MC: energy deposition in various EEMC layers

Event selection

Official EEMC gamma filtered Monte-Carlo with Pro-pT0 tune vs. Ilya's private production for Pro-pT0, Perugia0, and CDF Tune A
di-jets from the cone jet-finder algorithm
photon and jet are opposite in phi:
cos (phi_gamma-phi_jet) < -0.8
pt away side jet > 5GeV
detector eta of the away side jet: |eta_jet_det| < 0.8
data : L2e-gamma triggered events
Monte-Carlo: emulated L2e-gamma triggered condition
QCD Monte-Carlo scaled to the yield in the data (All Monte-Carlo normalized to the total yield in the data)
All plots with gamma pt >7GeV and jet pt >5GeV cuts

Figure 1: 3x3 tower cluster energy to jet R=0.7 energy ratio
Left: Official production (Pro-Pt0), Right: Pro-Pt0 (Ilya private production)

Figure 2: 3x3 tower cluster energy to jet R=0.7 energy ratio - Perugia0 (Ilya private production)

Figure 3: 3x3 tower cluster energy to jet R=0.7 energy ratio - CDF Tune A (Ilya private production)

Figure 4: 3x3 tower cluster with jet thresholds energy to jet R=0.7 energy ratio - Official production (Pro-Pt0)

Calorimeter studies for the STAR W analysis

Ilya Selyuzhenkov for the STAR Collaboration

Analysis links

Year 2010

January 2010
1. 2010/01/26: Endcap/Barrel clustering with official W-MC
2. 2010/01/18: W test sample: Cluster ratios and skewed gaussian fits
3. 2010/01/15: W test sample QA: Pass 3
4. 2010/01/13: W test sample QA: Pass 2
5. 2010/01/12: W test sample QA

Year 2009

December 2009
1. 2009/12/01: BEMC 1x1, 2x1, 2x2, 3x3 clustering
November 2009
1. 2009/11/17: BEMC sampling fraction: energy dependence
2. 2009/11/10: BEMC sampling fraction and clustering (y2009 geometry)
3. 2009/11/03: BEMC sampling fraction: with and without LOW_EM option (y2006g geometry)

Documentation for the photon-jet reconstruction code

Documentation for the photon-jet reconstruction code (Ilya Selyuzhenkov)

StEemcGammaFilter maker is the Pythia level EEMC gamma filter
StEemcGammaFilterMaker maker is the BFC level Endcap gamma filter
STAR Jet finder code and the new (Pibero's) jet tree format description
StEemcDstMaker is the maker which copies the Eemc detector response and TPC tracks
from MuDst into the StEemcDstEvent tree
StGammaJetMaker is the maker which reads jet, skim, and EemcDst trees simultaneously
and generates an StGammaJetEvent tree of gamma-jet candidate
StGammaJetAna is the maker which runs the shower shape and sided residual analyses.
As result it generates the StGammaJetAnaEvent tree
(StGammaJetAnaEvent is an event container which stores the gamma-jet sided residual info)
StGammaJetDraw is the maker which applies final analysis cuts and generates histograms
Signal to background optimization with multi-variant analysis:
macros to run TMVA analysis (LDA, neural network MLP, etc)
Macros to run various makers
Analysis flow chart

2009 Endcap geometry update (ecalgeo6.g)

2009/2010 GEANT geometry file update summary (ecalgeo v6.0):

Endcap geometry update and QA tests

Final updates (ecalgeo v6.2):

Analysis flow chart

Copy the StRoot directory from here (for example $my_dir)
Change to the parent directory for StRoot:

cd $my_dir

compile everything with cons
To run makers (expect starsim and bfc) use macros from here and run:

root4star -b -q 'RunXXXMaker.C("inputFileName.extension.root")'

Note: You can use files from iucf disk

Anylysis flow chart:

starsim (Kumac) -> fzd.gz

bfc.C (fzd) -> MuDst.root / geant.root

JetFinder (MuDst) -> jet.root / skim.root

EEmcDstMaker (MuDst) -> eemc.root

GammaJetMaker (jet/skim) -> dijet.root

GammaJetAnaMaker (dijet/eemc) -> ana.root

GammaJetDrawMaker (ana) -> draw.root / mlp.root

EEMC related MuDst/jet/skim/gamma trees location

Main directory for Ilya's private directory files at RCF IUCF disk:

/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC

CDF Tune A simulations (private production)

fzd.gz, geant.root, and MuDst.root are in files/ subdirectory
logs (MuDst.log.gz, sim.log.gz) in logs/ subdirectory

Prompt photons (partonic pt range 3-25, single bin):
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/20100630/GammaJet_pt3_25_pytune100

QCD (partonic pt range 4-35, bins: 4-6, 6-9, 9-15, 15-35):
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/20100630/QCD_pt4_6_pytune100
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/20100630/QCD_pt6_9_pytune100
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/20100630/QCD_pt9_15_pytune100
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/20100630/QCD_pt15_35_pytune100

Perugia0 simulations (private production)

fzd.gz, geant.root, and MuDst.root are in files/ subdirectory
logs (MuDst.log.gz, sim.log.gz) in logs/ subdirectory

Prompt photons (partonic pt range 3-25, single bin):
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/20100630/GammaJet_pt3_25_pytune320

QCD (partonic pt range 4-35, bins: 4-6, 6-9, 9-15, 15-35):
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/20100630/QCD_pt15_35_pytune320
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/20100630/QCD_pt4_6_pytune320
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/20100630/QCD_pt6_9_pytune320_1
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/20100630/QCD_pt6_9_pytune320_2
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/20100630/QCD_pt9_15_pytune320

Pro-pT0 simulations (private production)

fzd.gz, geant.root, and MuDst.root are in files/ subdirectory
logs (MuDst.log.gz, sim.log.gz) in logs/ subdirectory

Prompt photons (partonic pt range 3-25, single bin):
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/20100727/GammaJet_pt3_25_pytune329

QCD (partonic pt range 4-35, bins: 4-6, 6-9, 9-15, 15-35):
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/20100727/QCD_pt4_6_pytune329
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/20100727/QCD_pt6_9_pytune329
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/20100727/QCD_pt9_15_pytune329
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/20100727/QCD_pt15_35_pytune329

Pro-pT0 (official production)

trees (ana, dijet, draw, eemc, gamma, geant, jet, mlp, MuDst, skim) are in:
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/official/pp200/pythia6_423/

logs for:
ana, dijet, draw, eemc, gamma, jet, mlp, skim:
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/official/prodlog/trees/
MuDst and geant:
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/official/prodlog/P10ii/log/trs/

Prompt photons (partonic pt range 2-35, bins: 2-3, 3-4, 4-6, 6-9, 9-15, 15-35):
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/official/pp200/pythia6_423/pt_2-3gev/eemcgammafilt100_gamma
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/official/pp200/pythia6_423/pt_3-4gev/eemcgammafilt100_gamma
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/official/pp200/pythia6_423/pt_4-6gev/eemcgammafilt100_gamma
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/official/pp200/pythia6_423/pt_6-9gev/eemcgammafilt100_gamma
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/official/pp200/pythia6_423/pt_9-15gev/eemcgammafilt100_gamma
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/official/pp200/pythia6_423/pt_15-35gev/eemcgammafilt100_gamma

QCD (partonic pt range 4-35, bins: 4-6, 6-9, 9-15, 15-35):
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/official/pp200/pythia6_423/pt_4-6gev/eemcgammafilt100_qcd
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/official/pp200/pythia6_423/pt_6-9gev/eemcgammafilt100_qcd
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/official/pp200/pythia6_423/pt_9-15gev/eemcgammafilt100_qcd
/star/institutions/iucf/IlyaSelyuzhenkov/gammaFilterMC/official/pp200/pythia6_423/pt_15-35gev/eemcgammafilt100_qcd

Run 6 Jet/skim trees

/star/institutions/iucf/IlyaSelyuzhenkov/jetTrees/2006/ppProductionLong/

Run 6 gamma trees

/star/institutions/anl/Run6GammaTrees/log/
/star/institutions/anl/Run6GammaTrees/root/

Kumac file examples

Examples of different kumac files:

Single particle Monte-Carlo:

Combine singleParticle_begin.kumac with singleParticle_end.kumac
using needed geometry tag (example: detp geom y2006h)
Prompt photon Pythia Monte-Carlo testGammaJet.kumac

MSUB (14)=1
MSUB (18)=1
MSUB (29)=1
MSUB (114)=1
MSUB (115)=1
QCD 2->2 processes testQCD.kumac

MSUB (11) = 1
MSUB (12) = 1
MSUB (13) = 1
MSUB (28) = 1
MSUB (53) = 1
MSUB (68) = 1

L2Egamma trigger emulator howto

Running Run 6 L2 gamma trigger emulator

TObjArray* HList=new TObjArray;
StTriggerSimuMaker *simuTrig = new StTriggerSimuMaker("StarTrigSimu");
simuTrig->setHList(HList);
simuTrig->setMC(true); // must be before individual detectors, to be passed
simuTrig->useBbc();
simuTrig->useBemc();
simuTrig->useEemc(0);
simuTrig->bemc->setConfig(StBemcTriggerSimu::kOffline);
StGenericL2Emulator* simL2Mk = new StL2_2006EmulatorMaker;
assert(simL2Mk);
simL2Mk->setSetupPath("/afs/rhic.bnl.gov/star/users/kocolosk/public/StarTrigSimuSetup/");
simL2Mk->setOutPath("/star/institutions/iucf/IlyaSelyuzhenkov/data/MCFilter/StGenericL2Emulator_log/");
simuTrig->useL2(simL2Mk);

Macros to run TMVA analysis over the EEMC photon-jet events

TMVAnalysis/StRoot/macros

Macros to run various makers

macros to run various makers for the gamma-jet analysis:

StRoot/macros/L2GammaTests

StRoot/macros/draw

StRoot/macros/drawFilterQA

StRoot/macros/drawL2perRun

Macros to run various makers:

Run 6 and Run 9 bfc chain examples

Run 9 bfc options with EEMC slow and fast simulators (EEfs EEss):

"trs,fss,Idst,IAna,l0,tpcI,fcf,ftpc,Tree,logger,ITTF,Sti,MakeEvent,McEvent,
geant,evout,IdTruth,tags,bbcSim,tofsim,emcY2,EEfs,EEss,
GeantOut,big,-dstout,fzin,-MiniMcMk,beamLine,clearmem,eemcDB,VFPPVnoCTB"

Run 6 bfc options with EEMC slow and fast simulators (EEfs EEss):

"trs fss y2006h Idst IAna l0 tpcI fcf ftpc Tree logger
ITTF Sti VFPPV bbcSim tofsim tags emcY2 EEfs EEss evout
-dstout IdTruth geantout big fzin MiniMcMk clearmem eemcDb beamLine sdt20060523"

Run 6 bfc options with EEMC gamma filter in the chain (FiltEemcGamma):

"FiltEemcGamma trs fss y2006h Idst IAna l0 tpcI fcf ftpc Tree logger
ITTF Sti VFPPV bbcSim tofsim tags emcY2 EEfs EEss evout
-dstout IdTruth geantout big fzin MiniMcMk clearmem eemcDb beamLine sdt20060523"

Note:
need to use a special macro RunEemcGammaFilterBfc.C
to run FiltEemcGamma with bfc

Scheduler xml template file examples

Official scheduler documentation

Schedule template file examples:

Schedule-template_FullChain.xml -
advanced template to submit jobs for all (or selected) makers in one job (run over private Monte-Carlo files)
Schedule-template_FullChain_official.xml -
advanced template to submit jobs for all (or selected) makers in one job (for official Monte-Carlo)
Schedule-template_GammaJetDraw.xml - submit jobs for RunGammaJetDrawMC.C for private Monte-Carlo
Schedule-template_GammaJetDraw_official.xml - submit jobs for RunGammaJetDrawMC.C for private Monte-Carlo
Schedule-template_candidate.xml - submit jobs for RunGammaMaker.C / RunCreateFilterTree.C

Catalog request to get official Gamma filtered Monte-Carlo files:

get_file_list.pl -distinct -keys 'path,filename' -cond 'production=P10ii,path~pt_4-6gev/eemcgammafilt100_qcd/y2006h,filetype=MC_reco_MuDst,storage=nfs'

or

get_file_list.pl -distinct -keys 'path,filename' -cond 'production=P10ii,runnumber=2000010060,filetype=MC_reco_MuDst,storage=nfs

where run number = 2000000000 + 10060

StEemcDstMaker (Emc dst event container - similar to the gamma maker code structure)

StEemcDstMaker is Eemc dst event container (similar to the gamma maker code structure).

Creates root tree from MuDst which stores the relevant information
for the photon-jet analysis

StEemcDstEventMaker.h / StEemcDstEventMaker.cxx - gets pointer to the MuDst information
StEemcDstMaker.h / StEemcDstMaker.cxx - init EemcDstRawMaker
StEemcDstRawMaker.h / StEemcDstRawMaker.cxx - copy the information from MuDst with GetEndcap() and GetTracks() methods
StEemcDstStrip.h / StEemcDstStrip.cxx - container for the EEMC SMD strip
StEemcDstTower.h / StEemcDstTower.cxx - container for the EEMC tower and pre/post shower layers
StEemcDstTrack.h / StEemcDstTrack.cxx - container for the TPC track
StGeantRecord.h / StGeantRecord.cxx - copy the relevant GEANT/Pythia information from geant.root
StGeantRecordMaker.h / StGeantRecordMaker.cxx - TBD

StEemcGammaFilter (Pythia level EEMC gamma filter)

StEemcGammaFilter is the Pythia level EEMC gamma filter.

The code is available at STAR/cvs: StEemcGammaFilter.h / StEemcGammaFilter.cxx
Basic algo description:

Loop over particles and search for the ones with energy higher than threshold and falls into the fiducial (in rapidity) volume
Search for clusters around each seed include tracks in eta and phi (detector Eta and Phi) space within the cone radious

Algo parameters:

mConeRadius - eta-phi cluster cone radius
mSeedThreshold - seed track threshold
mClusterThreshold - track cluster threshold
mEtaLow - lowerst rapidity cut
mEtaHigh - highest rapidity cut
mMaxVertex - vertex cut

Other parameters:

mCalDepth - calorimeter depth at which tracks are extrapolated
mMinPartEnergy - minimum particle energyto be included in the cluster
mHadronScale - down scale factor for hadrons (be careful when playing with this) No scaling by default
mFilterMode - filter mode: 0 - test mode (no event rejection), 1 - filter reject events
mPrintLevel - print level (0 - no output, 1 or 2 print some logs)

StEemcGammaFilterMaker (BFC level Endcap gamma filter)

StEemcGammaFilterMaker big full chain (BFC) Endcap gamma filter

Code is accessible in CVS/STRoot: StEemcGammaFilterMaker.h / StEemcGammaFilterMaker.cxx

Parameter can be stored in the data base (see eemcGammaFilterMakerParams.idl file),
but this is not enabled in the current implementation.

Basic idea of StEemcGammaFilterMaker algo:

Search for the 3x3 tower cluster with high tower and cluster Et above thresholds

Available parameters to vary:
Seed energy threshold (mSeedEnergyThreshold, GeV)
Cluster eT threshold (mClusterEtThreshold GeV)
Maximum z vertex (mMaxVertex, cm)

Macro to run StEemcGammaFilterMaker with BFC
(fixes the problem of loading StJetSkimEvent library):

StRoot/StFilterMaker/macros/RunEemcGammaFilterBfc.C

StGammaJetAna (maker which does the shower shape analysis)

StGammaJetAna is the main maker which actually do the shower shape analysis.

StGammaJetAna.h / StGammaJetAna.cxx

StRoot/StGammaJetAna/StGammaJetAna:

CheckNeighbors.h - short method to get the adjacent towers
Cluster.h -
Constants.h - parameters
CreateHistLists.h -
Cuts.h
FillAnaTree.h
FillHists.h
FindMaxEnergySector.h
FindSmdCluster.h - redundant
FindSmdGammaCluster.h
FitSMDstripsGaus.h - redundant
FitSMDstripsSidedResidual.h
GetEemcEnergiesForMaxTower.h
GetPolarization.h
Init.h
Make.h
SetPreshowerBin.h
getNextEemcTowerId.h

StGammaJetAnaEvent (event container which stores the gamma-jet sided residual info)

StGammaJetAnaEvent is an event container which stores the information
on gamma-jet candidates, output from shower shape fits and sided residual analysis
Also it has an information from the crude pi0 (multi-photon event) finder

StGammaJetDraw (applying analsyis cutsand generate histograms)

StGammaJetDraw - applying analsyis cuts
(such as photon isolation, photon and jet pt cuts, etc)
and generate pre-shower sorted histograms

StGammaJetDraw.h / StGammaJetDraw.cxx

StRoot/StGammaJetDraw/StGammaJetDraw:

Constants.h
Constants_1Dhists.h
Constants_1Dhists_Geant.h
Constants_1Dprofiles.h
Constants_2Dhists.h
Constants_2Dhists_Geant.h
Constants_2Dprofiles.h
Constants_partonicPtBins.h
FillCandidateTxtFile.h - dumps gamma-jet info in txt file in format request by Hal Spinka
GetJetDetEta.h - short code calculates jet detector rapidity
GetPolarization.h
Init.h
Make.h
ReadSpinInfo.h
SetDrawOptions.h
SetPartonicWeight.h - sets partonic pt weigts for Monte-Carlo
createResidualHists.h
drawEemc.h
drawResidual.h - applying analysis cuts and fill histograms

StGammaJetEvent (simple container for the gamma-jet events)

StGammaJetEvent - container for the gamma-jet candidate events
(stores the information on selected di-jets from the jet finder and Eemdc Dst trees).

StGammaJetMaker (read jet, skim, and EemcDst trees and generate gamma-jet tree)

StGammaJetMaker read jet, skim, and EemcDst trees and select/write gamma-jet tree.

StGammaJetMaker.h / StGammaJetMaker.cxx
StRoot/StGammaJetMaker/StGammaJetMaker subdirectory has:

CheckNeighbors.h - simple code to check if the towers are adjacent
Constants.h - list of constant used by the StGammaJetMaker
getNextEemcTowerId.h - get the id of the adjacent tower
Make.h - pice of code which reads the trees and fills the EemcDst event

Photon-jets

Ilya Selyuzhenkov for the STAR Collaboration

Photon-jet reconstruction software documentation

Analysis links

Year 2010

September 2010
1. 2010/09/23: Neutral energy jet shape comparison for various tunes
2. 2010/09/15: Data vs. official filtered MC: energy deposition in various EEMC layers
3. 2010/09/13: Data vs. official gamma filtered Monte-Carlo: cluster energy ratios
4. 2010/09/10: Data to MC comparison with official EEMC gamma filtered production
5. 2010/09/08: First look at the official EEMC gamma filtered production
August 2010
1. 2010/08/10: Timestamps study for the EEMC gamma filtered simulation request
2. 2010/08/09: PyTune comparison with gamma candidates from dijets: Perugia0 vs. Pro-PT0
July 2010
1. 2010/07/23: PyTune comparison with photon candidates: Perugia0 vs. Pro-PT0
2. 2010/07/22: Run 6 EEMC gamma-filtered simulation request
3. 2010/07/20: Updated EEMC simulation spreadsheet: prompt photons and QCD
4. 2010/07/16: Pythia/BFC gamma-filter bias tests with realistic gain variation
5. 2010/07/14: Pythia/BFC gamma-filter bias tests (vs. gamma pt, eta, energy, and phi)
6. 2010/07/09: Table of L0-BBC, L0-EEMC, and L2Egamma triggers biases
7. 2010/07/02: Tests of L2e-gamma trigger emulation with full Pythia+Geant Monte-Carlo
June 2010
1. 2010/06/30: Py-tunes (GEANT+L2e-gamma trigger) vs. Run 6 data
2. 2010/06/28: Tests of L2e-gamma trigger emulation with single photon Monte-Carlo
3. 2010/06/18: L2e-gamma trigger effect: Py-CDF-Tune-A, Py-Perugia0, and pp2006 data comparison
4. 2010/06/17: Pythia TuneA/Perugia0 filtered MC vs. pp2006 data
5. 2010/06/15: First look at data vs. TuneA/Perugia0 filtered MC

May 2010
1. 2010/05/28: Photon-jet candidates - Perugia vs. Tune A
2. 2010/05/27: Run 6 and Run 9 jet Tree production: details specific to the photon-jet analysis
3. 2010/05/13: Pythia filter QA after fixed "Eta -> -Eta" bug
4. 2010/05/05: Starsim/bfc timing tests
5. 2010/05/03: EEMC simulation spreadsheet: prompt photons and QCD
6. 2010/05/03: Pythia/BFC filter biases vs. gamma candidate pt (with fixed gamma-maker)

April 2010
1. 2010/04/30: BFC and Pythia biases after gamma-maker 3x3 cluser fix has been updated
2. 2010/04/17: Pythia/BFC gamma-filter accaptance vs. gamma candidate pt, energy, and eta
3. 2010/04/17: BFC and Pythia QA: 10% gain shift: lowered thresholds
4. 2010/04/09: BFC and Pythia Filter QA: +10% gain shift (after filter code peer review)

March 2010
1. 2010/03/02: Photon-jet simulation request (draft)

February 2010
1. 2010/02/16: Pythia and BFC filter QA vs. gamma candidate pt and eta
2. 2010/02/12: Final Pythia and BFC EEMC-gamma-filter paramter settings
3. 2010/02/11: BFC and Pythia Filter QA: +5% gain shift (no spread, mean=1.05)
4. 2010/02/11: BFC and Pythia Filter QA: Gain study with "-5%" shift (mean=0.95, spread=0.05)
5. 2010/02/09: BFC and Pythia gamma filters: accept/reject rates relative to triggered events
6. 2010/02/08: Eemc-Gamma-Filter QA: QCD vs. gamma-jet ccept/reject rates

January 2010
1. 2010/01/18: EEMC response 2006 vs. 2009: typo in TPC volume size bug fix
2. 2010/01/08: EEMC response 2006 vs. 2009: TPC sector cuts
3. 2010/01/07: EEMC response to single photons with y2006h vs y2009a geometries
4. 2010/01/04: y2006h vs y2009a EEMC pre-shower migration

Year 2009

December 2009
1. 2009/12/20: Ecalgeo-v6.2: embedded LOW_EM cuts after TPC/EEMC overlap fix
2. 2009/12/17: Ecalgeo-v6.2: embedded LOW_EM cuts in the calorimeter geometry
3. 2009/12/08: Low EM timing study: 10KeV vs. 100KeV cut settings
4. 2009/12/07: Low EM study: LOW_EM option, 100KeV cuts, and DCUTE=100KeV
5. 2009/12/03: Photon-jet simulation request
November 2009
1. 2009/11/23: New EEMC geometry (CVS v6.1): y2006 vs. y2009 STAR configurations
2. 2009/11/19: LOW_EM and EEMC time/event in starsim
3. 2009/11/16: Tests of EEMC geometry, version 6.1: lead vs. mixture
4. 2009/11/11: Tests of EEMC geometry, version 6.1
5. 2009/11/06: new EEMC geometry: Pure lead and new SMD layers
6. 2009/11/03: Jason EEMC geometry: Effect of ELED block change
7. 2009/11/02: Jason EEMC geometry: results with and without LOW_EM options
October 2009
1. 2009/10/30: Jason EEMC geometry: Jason with ELED block from CVS file
2. 2009/10/27: Jason EEMC geometry: comparison without LOW_EM option
3. 2009/10/27: Jason EEMC geometry: effect of removing new SMD layers
4. 2009/10/26: Jason vs. CVS EEMC: removed SMD layers
5. 2009/10/20: Sampling fraction problem: full STAR vs. EEMC stand alone geometry
6. 2009/10/16: Jason geometry file: Full STAR simulations (sampling fraction, shower shapes)
7. 2009/10/13: Jason geometry tests: SMD energy, number of strip vs. thrown photon position
8. 2009/10/12: Effect of added layers in Jason geometry file
9. 2009/10/05: Volume id fix in Jason geometry file
10. 2009/10/02: Jason vs. CVS EEMC geometry: sampling fraction and shower shapes
September 2009
1. 2009/09/11: Test of corrected EEMC geometry: LOW_EM cuts
2. 2009/09/04: EEMC geometry tests: on-off SVT detector and EEMC slow-simulator
August 2009
1. 2009/08/27: Single particle MC with corrected geometry vs. eta-meson from data
2. 2009/08/25: Corrected EEMC geometry: shower shapes
3. 2009/08/24: Corrected EEMC geometry (bug 1618)
4. 2009/08/17: Plan for extracting photon-jet cross section from pp@200GeV data
July 2009
1. 2009/07/20: EEMC tower response in Monte-Carlo (pt_reco vs. pt_true)
June 2009
1. 2009/06/22: CIPANP 2009 proceedings on "photon-jet coincidence measurements"
May 2009
1. 2009/05/31: CIPANP 2009 presentation on "photon-jet coincidence measurements"
2. 2009/05/12: Various distributions after LDA cut at 70% efficiency (LDA Set1 vs. Set2 for various pre-shower bins)
3. 2009/05/07: Photon-jets analysis with the Endcap Calorimeter (analysis status update for Spin PWG)
4. 2009/05/06: Applying purity or efficiency driven cuts on LDA discriminant
5. 2009/05/04: LDA: added more SMD info, 3x3 tower energy, correlation matrix
6. 2009/05/03: Varying pt, eta cuts with LDA (LDA includes SMD, r2x1, r2x2, post-shower)
April 2009
1. 2009/04/28: LDA plus SMD analysis with pre-shower sorting
2. 2009/04/22: Combining LDA with shower shape analysis
3. 2009/04/17: The STAR spin program with long. polarized proton beams (seminar at Wayne State U.)
March 2009
1. 2009/03/26: Photon-jets with the STAR Endcap Calorimeter (STAR Collaboration Meeting at BNL)
2. 2009/03/09: Application of the LDA and MLP classifiers for the cut optimization
3. 2009/03/02: Neural network application for the cut optimization (zero try)
February 2009
1. 2009/02/19: Photon-jets from the Endcap Calorimeter (Analysis status update for Spin PWG)
2. 2009/02/16: pp2006, pp2008, and mc2006: pt_jet>5GeV; pre-shower sorting
3. 2009/02/12: CIPANP 2009 abstract
4. 2009/02/09: pp2006, pp2008, and mc2006 comparison
5. 2009/02/06: Pre-shower energy: Run6 vs. Run8 geometry
6. 2009/02/03: Data to Monte-Carlo matching: pt_jet>7GeV vs. no pt_jet cut
7. 2009/02/02: Data to Monte-Carlo matching: fudge factor 1.24 (no pt_jet cuts, no pre-shower cuts)
January 2009
1. 2009/01/27: Away side jet and partonic pt contributions (fiducial cuts: |eta_jet| < 0.7, pre1<10MeV)
2. 2009/01/27: Away side jet and partonic pt contributions: pt_jet>7GeV (detector |eta_jet| < 0.8)
3. 2009/01/20: Data to Monte-Carlo normalization (pt and vertex z distributions, no isolation cuts)
4. 2009/01/08: Away side jet pt vs. photon pt (no isolation cuts)

Year 2008

December 2008
1. 2008/12/19: Parton pt distribution: QCD vs. gamma-jet events (more QCD statistics from Michael)
2. 2008/12/16: Effect of L2gamma trigger in simulations
3. 2008/12/11: ppProduction2008 QA (presentation for Spin PWG)
4. 2008/12/08: pp2008 data QA (x vs. y photon position, pre1:sector 10 problem, vertex z for pp06/pp08 data)
November 2008
1. 2008/11/25: Yields vs. analysis cuts: eta dependence
2. 2008/11/21: Cluster Isolation cut from 2x1, 2x2, and 3x3 patch: rapidity dependence
3. 2008/11/18: Isolation cut for 2x1, 2x2 and 3x3 tower clusters
4. 2008/11/11: Yields vs. analysis cuts (event counter, data/MC ratio)
5. 2008/11/06: Gamma-jet reconstruction with the Endcap EMC (Analysis status update for Spin PWG)
October 2008
1. 2008/10/30: SMD energy and shower shapes for various cuts: pt>8.5, Esmd>0.1, 8 vs. 12 strips replaced
2. 2008/10/27: SMD-based shower shape scaling (25 strips cluster energy, raw vs. data-driven MC)
3. 2008/10/21: Shower shapes, 5/25 strips cluster energy, raw vs. data-driven MC (with new filtered events)
4. 2008/10/15: Purity/efficiency/rejection: R_cluster > 0.9 vs. 0.98 (update with gamma-filtered events)
5. 2008/10/15: Gamma-jets from Michael's filtered events vs. old MC gamma-jet and QCD jets events
6. 2008/10/14: Purity/efficiency/rejection: R_cluster > 0.98
7. 2008/10/13: Jet and skim trees for Michael's gamma filtered events
September 2008
1. 2008/09/30: Sided residual: purity, efficiency, and background rejection
2. 2008/09/23: Right-left SMD tail asymmetries
3. 2008/09/23: Sided residual plot study
4. 2008/09/23: QA plots for maximum sided residual (bug fixed update)
5. 2008/09/16: QA plots for maximum sided residual (obsolete)
6. 2008/09/09: Maximum sided residual from shower shapes sorted by uv- and pre-shower bins
7. 2008/09/02: Shower shape's triple Gaussian fits
August 2008
1. 2008/08/27: Gamma-jet candidates detector position for different pre-shower conditions
2. 2008/08/26: Shower shapes: more constrains for pre1>4E-3 bin (2D and projections for sided residual, pre1<10MeV cut)
3. 2008/08/25: Di-jets QA from pp2008 vs. pp2006 data
4. 2008/08/19: Shower shape from pp2008 vs pp2006 data
5. 2008/08/14: Shower shape with bug fixed dd-library (raw shapes, pre1=4MeV cut, u/v shapes within 12 and 40 strips from peak)
July 2008
1. 2008/07/29: Shower shape comparison with new dd-library (pre1=4MeV cut, u/v shapes within 12 and 40 strips from peak)
2. 2008/07/22: Photons from eta-meson: library QA (shower shapes [pre1=4E-3 cut], energy plots, etc)
3. 2008/07/16: Gamma-gamma invariant mass cut study (inv mass, pt, shower shapes plots)
4. 2008/07/07: Pre-shower1 < 5MeV cut study (shower shapes, pre1:pre2, pre1, pt, sided residual plots)
June 2008
1. 2008/06/18: Photon-jet reconstruction with the EEMC detector (Davis Collaboration Meeting)
2. 2008/06/10: Longitudinal double spin asymmetry for gamma-jet candidates
3. 2008/06/09: STAR White paper plots (pt distribution: R_cluster 0.99 and 0.9 cuts)
4. 2008/06/04: 3x3 cluster energy in various EEMC layers (MC to data matching)
May 2008
1. 2008/05/30: Gamma and away side jet pt, eta and phi (MC to data matching)
2. 2008/05/27: Shower shapes comparison (pp2006, MC gamma-jet, QCD jets, eta-meson gammas)
3. 2008/05/21: Some eta-meson QA plots (invariant mass, detector eta, etc)
4. 2008/05/20: Shower shapes (sorted by: pre-shower, z-vertex; gamma pt, eta, and phi)
5. 2008/05/15: Vertex z distribution for gamma-jet candidates
6. 2008/05/14: R_cluster>0.99 and pt matching (gamma and jet pt asymmetry)
7. 2008/05/09: TPC tracking (detector eta 1<1.4)
8. 2008/05/08: SMD high strips positioning (x:y and in-sector distributions)
9. 2008/05/05: pt distributions and 1D-sided residual plots (data vs. MC g-jet and QCD jets)
April 2008
1. 2008/04/23: Sided residual and gamma:jet pt 2D plots (data driven MC vs. data)
2. 2008/04/17: Sided residual (data driven vs. raw MC, pp2006) (plus: Geant vs. reconstructed pt, event counter)
3. 2008/04/16: Sided residual (partonic pt 9-11) (plus: event counter)
4. 2008/04/09: Reconstructing raw MC gamma-jet events (shapes, gamma:jet pt, phi, eta, sided residual, logD, chi2 plots)
5. 2008/04/03: chi2-shape subtraction (request from Hal)
6. 2008/04/02: Sided residual: 3 Gaussian fits and inv. mass sorting (plus: projections and signal/bg separation)
7. 2008/04/02: Shower shapes: eta-meson and pp2006 vs. MC gamma-jets and single gammas
8. 2008/04/02: Single gamma MC (sided residual plot)
March 2008
1. 2008/03/28: Shower shapes: pp2006 gamma-jets, MC (from Pibero), and eta-meson (plus: stip-by-strip energy, chi2 plots)
2. 2008/03/26: Sided residual (2 Gaussian fits, with/without pt cuts) and chi2 (plus: pt, eta, phi, by-sector 2D plots)
3. 2008/03/20: First look at sided residual and chi2 (plus: pdf-file with event-by-event EEMC response)
4. 2008/03/12: M_2gamma invariant mass plots (plus: pdf-files with event-by-event EEMC response)
5. 2008/03/03: EEMC SMD QA plots (mean energy for SMD high strips)
February 2008
1. 2008/02/27: EEMC vs. BEMC gamma-jet candidates (pt, eta, phi, 3x3 R_cluster vs. nTowers, various pt-cuts)
2. 2008/02/20: NEF, pt, eta, phi for gamma-jet candidates (more statistics)
3. 2008/02/13: Vertex z, m_2gamma inv. mass from simple pi0 finder (plus: pdf-files with event-by-event EEMC response)
January 2008
1. 2008/01/30: Gamma-jet candidates: numberOfJets/event, NEF, pt, eta, phi (first look, only runId=7136022)

Selected figures

Figure 1:. Gamma candidate pt distributions for a three different data samples:

pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts.
gamma-jet - Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.
QCD jets - Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV.

Note: Same algorithm has been used to analyse Monte-Carlo and real data events

Figure 2: Sided residual and sample gamma-jet candidate (EEMC response)

Various

2009.08.17 Direct photon - charge particle correlation paper GPC

proposed modification ot the title/abstract

Comments on Neutral Pion Production in Au+Au Collisions

EEMC geometry file (ecalgeo)

l2-gamma EEMC monitoring

Instructions on how to produce eemc-l2-gamma monitoring plots by hands

Get l2 software
Currently copied from ~rcorliss/l2/official-2009a
Compile
# cd onlineL2
# make lib
# make
Run
# m 5000
Convert "bin" file format to root file:
# ./binH2rootH-exe out/run9.l2eemcGamma.hist.bin out/run9.l2eemcGamma.hist.root
Create ps file with plots from root file with plotl2eemcGamma.C macro:
# root -b -q 'plotl2eemcGamma.C("out/run9.l2eemcGamma.hist.root","run9.l2eemcGamma.ps")'
# ps2pdf run9.l2eemcGamma.ps run9.l2eemcGamma.pdf
Sample pdf plots:
pythia_pT7-9_rcf1225.eve_.bin-348k
pythia500_QCD_pt10_Filter20Bemc_LT10invpb

run9 l2 rates monitoring

Relative Luminosity Analysis

Documentation for Relative Luminosity Analysis at STAR:

Online Scaler Analysis: http://online.star.bnl.gov/scaler/
Tai's Webpage : http://people.physics.tamu.edu/sakuma/star/scaler/2006/
2009 Analysis notes: see attached 2009_RelLumi.pdf and Jim's precurser to this note.

Run 6 Dijet Cross Section (Tai Sakuma)

Dijet Cross Sections in Proton-proton collisions at √s = 200 GeV

Tai Sakuma

Preliminary Results

The dijet cross sections were measured using a data sample of 5.4 pb^-1 in proton-proton collisions at 200 GeV. The cross sections were measured at the mid-rapidity |η| ≤ 0.8 as a function of dijet mass M_jj.

The dijet cross sections were estimated with the formula:

J is the dijet yields at the detector level. C is the correction factors which correct the dijet yields to the hadron level. The correction factors C are estimated from the MC events as bin-by-bin ratios of the dijet yields at the detector level and at the hadron level. ΔM_jjΔη₃Δη₄ normalizes the cross sections to per unit space volume.

The major systematic uncertainty was due to the uncertainty on the jet energy scale (JES). Because the M_jj dependence is steeply decreasing, the uncertainty of the cross section is very sensitive to systematic uncertainty on the JES.

The dijet cross sections compared to theoretical predictions. The systematic uncertainty does not include 7.68% of uncertainly due to the uncertainly on the integrated luminosity.

(pdf)

The ratios: (data - theory)/theory. The theory includes the NLO pQCD predictions and the corrections for the effects of hadronization and underlying events. The ratios are taken bin by bin.

(pdf)

The figures show the preliminary results of the dijet cross section measurements. The measured dijet cross sections are well described by the theory. This indicates that measured A_LL as well can be interpreted in the same theory and suggests ways to constrain the polarized gluon distributions from dijet A_LL.

Analysis Detail

Please, visit this site for the detail: http://people.physics.tamu.edu/sakuma/star/jets/c100923_dijets/s0000_index_001/prelim.php

Run 6 Dijet Double Longitudinal Spin Asymmetry (Tai Sakuma)

Dijet Double Longitudinal Spin Asymmetry A_LL in Proton-proton collisions at √s = 200 GeV

Tai Sakuma

Preliminary Results

The longitudinal double spin asymmetry A_LL of the dijet production in polarized proton-proton collisions at 200 GeV at the mid-rapidity |η| ≤ 0.8 was measured. A_LL was measured as a function of the dijet invariant mass M_jj.

The A_LL was measured as the ratios of the spin sorted dijet yields with the corrections for the relative luminosity and polarizations:

Four false asymmetries, which should vanish, were measured for a systematic check of the data. Two single spin asymmetries and two wrong-sign spin asymmetries were consistent with zero within the statistical uncertainties.

The largest systematic uncertainty is the trigger bias, which is the uncertainty in the changes of A_LL from the parton level to the detector level. This was evaluated using the MC events with several polarized parton distributions which are compatible with the current experimental data: DSSV, GRSV std, and the GRSV series with ΔG from -0.45 to 0.3. In this evaluation, in order to calculate A_LL with the unpolarized event generator Pythia, the MC events were wighted by the products of the spin asymmetries of the parton distributions and parton-level cross sections.

The double longitudinal spin asymmetry A_LL for the dijet production as a function of dijet mass M_jj.

(pdf)

The result is consistent with a theoretical prediction based on the DSSV polarized parton distributions, which indicates the dijet A_LL will be a promising channel to constrain the polarized gluon distribution when we collect more statistics.

Analysis Detail

Please, visit this site for the detail: http://people.physics.tamu.edu/sakuma/star/jets/c100926_dijets_asym/s0000_index_001/prelim.php

Run 6 Inclusive Jet Cross Section (Tai Sakuma)

Inclusive Jet Cross Sections in Proton-proton collisions at √s = 200 GeV

Tai Sakuma

Preliminary Results

Introduction

We measured the inclusive jet cross section in proton-proton collisions at 200 GeV using data collected with the STAR detector during RHIC Run-6. The jet cross section is an essential quantity to test the predictive power of Quantum Chromodynamics (QCD). We have made several improvements since the previous measurement from STAR [PRL 97 (2006) 252001]; the data size increased from 0.3 pb^-1 to 5.4 pb^-1; while the previous measurement used the Time Projection Chamber (TPC) and only the west side of the Barrel Electromagnetic Calorimeter (BEMC), which corresponds to the acceptance of 0 ≤ η ≤ 1, this measurement used the TPC and both sides of the BEMC (-1 ≤ η ≤ 1).

Jet Definition

Jets are sprays of particles which are moving approximately in the same direction from the collision point. We used the mid-point cone jet-finding algorithm with the cone radius 0.7 to define jets. Jets can be defined at three different levels: the parton level, the hadron level, and the detector level. The parton-level jets are outgoing partons of the hard interactions. The hadron-level jets are composed of products of hadronization and particle decay of the outgoing partons. They are predominantly hadrons, but may contain leptons and photons as well. The detector-level jets are detector responses to the hadron-level jets. They are made of energy deposited in BEMC towers and charged tracks reconstructed in the TPC.

Hadron-level Jet Yields

We estimated the hadron-level jet yields from the detector-level jet yields by inverting the response of the detector using a MC simulation. Consequently, the results have a tendency to be biased toward the predictions of the MC simulation. To minimize this bias, we used MC events which desciribe the data well.

Results

(pdf)

The results are in agreement with next-to-leading-order (NLO) perturbative QCD predictions which include corrections for non-perturbative effects. This agreement is evidence that the measured inclusive jet A_LL [PRL 97 (2006) 252001] [PRL 100 (2008) 232003] can be interpreted in the framework of the QCD factorization. Furthermore, having a theoretical model that well describes the inclusive jet production is a crucial step toward dijet measurements.

Poster

There is a poster about the preliminary results.

A full size pdf file can be found at (pdf).

Analysis Detail

Please, visit this site for the detail: http://people.physics.tamu.edu/sakuma/star/jets/c100826_inclusive_jets/s0000_index_001/prelim.php

MC production for the di-jet cross section measurements in pp collisions at 200 GeV

Introduction

Originally, the MC samples generated for the inclusive jet analyses were used for the di-jet analyses. However it turns out that these samples were not sufficient for the di-jet analyses especially for unfolding the cross sections for the 2005 di-jets. To generate sufficient MC samples with available computing resources, MC generations were designed to generate events only in the region of the phase space where the di-jet events in the STAR acceptance come from.

Original MC sample

These are the distributions of pseudorapidity and scattering angle for the original MC samples. The left plots of each image is for all the generated events and the right plots are for the dijet events.

Significant portion of the dijet events come from only the regions inside the red boxes.

New MC sample

The new MC samples were generated only in those regions of the phase space from which most dijet events come.

How to run the patched PYTHIA 6 in starsim

The most of the MC production jobs that the spin PWG requested for the di-jet analysis didn't finish successfully
Patch for a bug in PYTHIA6
This page explains how to run the patched PYHIA 6 as the event generator for starsim. The starsim has a mechanism where a user can choose a specific version of Pythia to use at run time.This feature will be used.

Install shared objects for the patched PYTHIA for starsim

This tar ball contains the source files for the patched PYTHIA 6.205 and 6.4.10 in the form that fits to starsim.

patched_pythia_for_starsim.1.0.0.tar.gz

Build the shared objects with the following commands.

tai@rcas6007% stardev
tai@rcas6007% tar xvfz patched_pythia_for_starsim.1.0.0.tar.gz
tai@rcas6007% cons

Two shared objects should be built in the directory $MINE_LIB.

pythia_6205t.so
pythia_6410t.so

These are loadable modules for starsim which contain the patched PYTHIA 6.205 and 6.4.10, respectively.

run starsim

This is a smple kumac file load the shared object pythia_6410t.so and generated one hundred events.

Download sample kuip macro

tai@rcas6007% starsim
 ******************************************************
 * Starting  starsim NwGEANT=  20000000 NwPAW= 2000000 *
 **********************pid= 22383**********************
 ******************************************************
 *                                                    *
 *            W E L C O M E    to   starsim           *
 *                                                    *
 *        Version INITIAL     16 November 2006        *
 *                                                    *
 ******************************************************
 Workstation type (?=HELP) <CR>=1 : 0
 Version 1.29/04 of HIGZ started
1*****  GEANT Version  3.21/08 Released on 230697
0*****  Correction Cradle Version  0.0800
 *****  RZMAKE. OLD RZ format selected for RZDOC     
starsim > exec sample.kumac

You should be able to find the output my_pythia_file.fz if the MC production is successful.

If you have a question or comment, send email to sakuma@bnl.gov or add comment to this page.

Patch for a bug in PYTHIA6

This patch is included in PYTHIA from PYTHIA 6.412. You don't need to apply this patch if you are using PYTHIA 6.412 or newer.

Download Patch

pythia-6.4.11-t.diff : patch for PYTHIA-6.4.11
pythia-6205-6205t.diff : patch for PYTHIA-6.205

If you need the patch for a different version of PYTHIA, email sakuma@bnl.gov.

Introduction

At the end of the year 2006, The spin PWG requested the MC productions with uncommon kinematical cuts for the measurement of
the 2005 di-jet cross sections in pp collisions.
The most of the MC production jobs didn't finish successfully.
The failure is due to a bug in PYTHIA 6. This page provides a patch for the bug.

If you want to run the patched PYTHIA as the event generator for starsim, the instruction can be found at "How to run the patched PYTHIA 6 in starsim."

Bug description

PYTHIA 6 has options to generate events only a specific region of the phase space. You can specify the region in terms of various variables such as pT, y, cos(theta), s, t, u, x1, x2, and other kinematical variables. With some sets of the values for the cuts, PYTHIA 6 behaves improperly especially when cuts are applied on both pT and cos(theta) simultaneously. This is a main program that can reproduce the improper behavior.

main.f : A main program for PYTHIA 6 that reproduces the improper behavior

This main.f should be able to reproduce the bug for any version of PYTHIA between 6.2 and 6.4.11. From the version 6.4.12, this patch is included in PYTHIA.
You can compile and run for yourself with the following commands.

f77 main.f pythia-6.4.11.f
./a.out

PYTHIA 6 can be found at the following websites.

download PYTHIA 6.4
download PYTHIA 6.3, 6.2

With the main.f, PYTHIA will try to generate one thousand events of the pp collisions at 200 GeV in the following region of the phase space.

11.0 < pT < 15.0
-0.2 < y < 1.2
-0.6 < cos(theta) < -0.3

With the main.f, the cross section does not converge and sometime
becomes negative.
And you will also constantly get the warning like

 Warning: negative cross-section fraction -2.262E+01 in event     140
 ISUB =  28; Point of violation:
 tau =  2.930E-02, y* =  3.735E-01, cthe =  -0.5988706, tau' =  0.000E+00

and

 Advisory warning: maximum violated by  1.505E+01 in event     243
 XSEC(68,1) increased to  1.631E-01

Then, PYTHIA will gradually slow down in generating events and won't make
it to generate one thousand events within an hour on an average PC.

Patch for the bug

These are the patches for the bug for PYHIA 6.4.11 and 6.205.

pythia-6.4.11-t.diff : patch for PYTHIA-6.4.11
pythia-6205-6205t.diff : patch for PYTHIA-6.205

You can apply these patches, for example, in this way.

patch pythia-6.4.11.f < pythia-6.4.11-t.diff

Comments from the author of PYTHIA

I reported the bug and sent my patch to Torbjörn Sjöstrand, the author of PYTHIA. He responded "Thanks a lot for your input. Yes, you are right, this is a
possibility we missed so far. Will try to have your fix inserted for the next subversion. ... In retrospect the number of kinematics options grew to a level
where not all possible combinations were sufficiently tested".

In another email, I asked him if my patch is safe to use for serious physics study. Torbjörn Sjöstrand said
"So far as I can see the patch should be safe and not have any
undesirable consequences, but I could not give any 100%
guarantees".

The patch that I actually sent to the author is simpler than ones in the previous section.

pythia-6.4.11.diff : patch that I sent to the author of PYTHIA

In the patches in the previous section, I also modified the title logo such that it indicates that the program is modified.

Verification of the patch

I compared the cross sections computed with the original PYTHIA and the patched PYTHIA. I got a consistent result. I'll write the detail later.

How to implement the patch to Starsim

I figured out how to implement this patch in starsim.
How to run the patched PYTHIA 6 in starsim

Run 6 Neutral Pions

Neutral pion analysis

2006 Neutral Pion Paper Page

Proposed Title: "Double longitudinal spin asyymetry and cross sectrion for inclusive neutral pion production at midrapidity in polarized proton collisions at Sqrt(s) = 200GeV"

PAs: Alan Hoffman, Joe Seele, Bernd Surrow, ...

Target Journal: PRD - Rapid Communication

Abstract:

Figures:

The first two figures are obvious: the cross section plot and the A_LL plot. The third is less so. I offer a number of options below.

Caption 1. Cross section for inclusive \pi^0 production. a) The unpolarized cross section vs. P_T. The cross section points are plotted along with NLO pQCD predictions made using DSS and KKP fragmentation functions. b) Statistical and systematic uncertainties for the cross section measurement. c) Comparison of measured values to theoretical predictions. Predictionsa re shown for three different fragmetnation scales to give an idea of theoretical uncertainty.

Caption 2. The longitudinal double spin asymmetry, A_LL, vs. P_T for inclusive \pi^0 prodution. The error bars are purely statistical. The systmatic uncertainty is represented by the shaded band beneath the points. The measurement is compared to a number of NLO pQCD predictions for different input values of \Delta G.

3) Option 1

Caption: Comparison between Data and Simulation for energy asymmetry Z_gg = |E1 - E2|/(E1 + E2). (Ed. Note: The nice thing about this plot is that it would not require too much supporting explanation in the text. Readers would know what it means. "Monte Carlo" could be changed to "Pythia + Geant" to be more specific.)

3) Option 2

Caption: Left: Two photon invariant mass distribution for data (black points) and various simulation components. Right: Same data distribution compared to combined simulation. (Ed. Note: This plot would require us to describe the whole mass-peak simulation scheme, which may be outside the scope of the paper.)

3) Option 3

Some combination of these two plots showing the A_LL points and then the P-values associated with various pQCD predictions. This would follow the example set by the jet group.

Summary and Conclusions:

Supporting Documentation:

Alan Hoffman's Thesis

A_LL analysis page

Cross Section Analysis page

2006 Neutral Pion Update (9/27/07)

Click on the link to download my slides for the PWG meeting.

Presentation

A_LL

BEMC Related Studies

My initial attempt at pinpointing the causes of the 'floating' pion mass examined, as possible causes, the fit function, an artificial increase in the opening angle, and the BEMC energy resolution. Preliminary findings indicate that the both the opening angle and the BEMC resolution play a part in the phenomena, however the resolution seems to be the dominant factor in these pt ranges (5.2 - 16).

Pion Pt Study

I also compared the effect in Data and MC using a full QCD (pythia + geant) MC sample, filtered for high-pt pions. As can be seen in the link below, the mean mass position and peak widths are comprable in data and MC. The floating mass problem is readily reproduced in this MC sample.

Data/MC comparison

Cross Section Analysis

On the pages listed below I'm going to try to keep track of the work towards measuring the cross section for inclusive pion production in pp collisions from run 6. I'll try to keep the posts in chronological order, so posts near the bottom are more recent than posts near the top.

All Errors

The plot below shows the total errors, statistical and systematic, for the cross section measurement. The inner tick marks on the error bars represent the statistical errors. The outer ticks represent the statistical and point-to-point systematic errors added in quadrature. The grey band shows the systematic error from the BEMC gain calibration uncertainty, which we have agreed to separate from the others (as it cannot be meaningfully added in quadrature to the other uncertainties).

BEMC Calibration Uncertainty

Goal:

To calculate the systematic uncertainty on the cross section due to the BEMC calibration uncertainty.

Justification:

Clearly, the BEMC energy scale is of vital importance to this measurement. The energies measured by the BEMC are used to calculate many of physics level quantities (photon energy, pion pt, pion mass, Zgg) that directly enter into the determination of the cross section. Since the calibration of the BEMC is not (and indeed could not be) perfect, some level of uncertainty will be propagated to the cross section. This page will try to explain how this uncertainty is calculated and the results of those calculations.

Method:

Recently, the MIT grad students (lead by Matt Walker) calculated the gain calibration uncertainty for the BEMC towers. The results of this study can be found in this paper. For run 6, it was determined that the BEMC gain calibration uncertainty was 1.6% (in the interest of a conservative preliminary estimate I have gone with 2%). In the analysis chain, the BEMC gains are applied early, in the stage where pion candidate trees are built from the MuDSTs. So if we are to measure the effect of a gain shift, we must do so at this level. To calculate the systematic uncertainty, I recalculated the cross section from the MuDSTs with the BEMC gain tables shifted by plus or minus 2%. I recreated every step of the analysis from creating pion trees to recreating MC samples, calculating raw yields and correction factors, all with shifted gain tables. Then, I took the ratio of new cross section to old cross section for both shifts. I fit these ratios after the first two bins, which are not indicative of the overall uncertainty. I used these fits to estimate the systematic for all bins. The final results of these calculation can be found below.

Plots:

1) All three cross secton plots on the same graph (black = nominal, red = -2%, blue = +2%)

2) The relative error for the plus and minus 2% scenarios.

Discussion:

This method of estimating the systematic has its drawbacks, chief among which is its maximum-extent nature. The error calculated by taking the "worst case scenario," which we are doing here, yields a worst case scenario systematic. This is in contrast to other systmatics (see here) which are true one-sigma errors of a gaussian error distribution. Gaussian errors can be added in quadrature to give a combined error (in theory, the stastical error and any gaussian systematics can be combined in this manner as wel.) Maximum extent errors cannont be combinded in quadrature with gaussian one-sigma errors (despite the tendency of previous analyzers to do exactly this.) Thus we are left with separate sources of systematics as shown below. Furthermore, clearly this method accurately estimates the uncertainty for low pt points. Consider, for example, the +2% gain shift. This shift increases the reconstructed energies of the towers, and ought to increase the number of pion candidates in the lower bins. However, trigger thresholds are based on the nominal energy values not the increased energy values. Since we cannot 'go back in time' and include events that would have fired the trigger had the gains been shifted 2% high, the data will be missing some fraction of events that it should include. I'm not explaining myself very well here. Let me say it this way: we can correct for thing like acceptence and trigger efficiency because we are missing events and pions that we know (from simulation) that we are missing. However, we cannot correct for events and candidates that we don't know we're missing. Events that would have fired a gain shifted trigger, but didn't fire the nominal trigger are of the second type. We only have one data set, we can't correct for the number of events we didn't know we missed.

All this being said, this is the best way we have currently for estimating the BEMC energy scale uncertainty using the available computing resources, and it is the method I will use for this result.

Candidate Level Comparisons

Objective:

Show that, for an important set of kinematic variable distributions, the full QCD 2->2 Pythia MC sample matches the data. This justifies using this MC sample to calculate 'true' distributions and hence correction factors. All plots below contain information from all candidates, that is, all diphoton pairs that pass the cuts below.

Details:

Data (always black points) Vs. T2_platinum MC (always red) (see here)

Cuts:

Events pass L2gamma software trigger and (for data) online trigger.
candidate pt > 5.5
charged track veto
at least one good strip in each smd plane
Z vertex found
| Z vertex | < 60 cm

Plots:

1a)

The above plot shows the Zgg distribution ((E1 - E2)/(E1 + E2)) for data (black) and (MC). The MC is normalized to the data (total integral.) Despite what the title says this plot is not vs. pt, it is integrated over all values of pt.

1b)

The above left shows Data/MC for Zgg between 0 and 1. The results have been fit to a flat line and the results of that fit are seen in the box above. The above right shows the histogram projection of the Data/MC and that has been fit to a guasian; the results of that fit are shown.

2a)

The above plot shows the particle eta for data pions (black) and MC pions (red). The MC is normalized to the data (total integral.) As you can see, there is a small discrepancy between the two histograms at negative values of particle eta. This could be a symptom of only using one status table for the MC while the data uses a number of status tables in towers and SMD.

2b)

The above left plot shows Data/MC for particle eta, which is fit to a flat line. The Y axis on this plot has been truncated at 2.5 to show the relevant region (-1,1). the outer limits of this plot have points with values greater than 4. The above right shows a profile histogram of the left plot fit to a gaussian. Note again that some entries are lost to the far right on this plot.

The above plot shows detector eta for data (black) and MC (red). Again we see a slight discrepancy at negative values of eta.

3b)

The above left shows Data/MC for detector eta, and this has been fit to a flat line. Again note that the Y axis has been truncated to show detail in the relevant range. The above right shows a profile histogram of the left plot and has been fit to a guassian.

The above plot shows the raw yields (not background subtracted) for data (black) and MC (red) where raw yield is the number of inclusive counts passing all cuts with .08 < inv mass < .25 and Zgg < 0.7. There is a clear 'turn on' curve near the trigger threshold, and the MC follows the data very nicely. For more information about the individual mass peaks see here.

Conclusions:

The Monte Carlo sample clearly recreates the data in the above distributions. There are slight discrepancies in the eta distributions, but they shouldn't preclude using this sample to calculate correction factors for the cross section measurement.

Collaboration Meeting Presentation and wrap up.

Attached is the presentation I gave at the collaboration meeting in late March. It was decided that before releasing a preliminary result, we would have to calculate the BEMC gain calibration uncertainty for 2006. A group of us at MIT created a plan for estimating this uncertainty (see here) and are currently working on implementing this plan as can be seen in Adam's blog post here:

http://drupal.star.bnl.gov/STAR/blog-entry/kocolosk/2009/apr/06/calib-uncertainty-update.

We hope to have a final estimate of this uncertainty by early next week. In that case, I can hopefully calculate the uncertainty in the cross section by the spin pwg meeting on 4/23. If this can be done, I will present the results then and ask to release the preliminary result for DIS 2009. If we cannot calculate a gain calibration systematic by 4/23, obviously we will not be asking to release the preliminary result.

Comparison with Other Results

Below you will find comparisons of the Run 6 cross section with recent Phenix results and the STAR Run 5 final result. The lower panel is the statistical error on the Run 6 points. Uncertainties for the other plots are not included. Plot 1 shows a comparison between run 6 and the Phenix result as published in PRD. Plot 2 shows a comparison between run 6 and the Run 5 final result from STAR, and plot three 3 shows all three results together. As requested, I included the errors for both run 5 and 6 results. The error bars on the cross section points are a quadrature sum of all errors (stat and sys) for the results.

Concerning the 'floating' mass peak and Zgg

Objective:

Explore the pt-dependent mean mass position in data and MC and perhaps draw some conclusions about the quality of our simulations.

Details:

Data Vs T2 platinum MC (see here for explanations)

Bins in Pt {5.5, 6., 6.75, 7.5, 8.25, 9.25, 11., 13., 16., 21.}

Cuts:

Events pass L2gamma software trigger and (for data) online trigger.
candidate pt > 5.5
charged track veto
at least one good strip in each smd plane
Z vertex found
| Z vertex | < 60 cm

Plots:

The above plot shows data (black) Vs. MC (red) for Zgg for my 9 pt bins. The MC plots are normalized to the data so that the total number of entries is equal in both distributions.

Apologies for the poor labeling. The above left plot shows the mean mass per Pt for data (black) and MC (red). These means are calculated by fitting the mass spectra to a gaussian between .1 and .2 GeV/c^2. (see here for more) In addition to the cuts listed at the top of page, I make a Zgg < .7 cut and an | particle eta | < .7 cut on all pion candidates. The PDG mass of the pi0 is shown in blue. The above right plot shows the ratio of Data/MC for the mean masses, again as a function of Pt. This plot is fit to a flat line and the fit parameters are shown.

Conclusions:

The most basic conclusion we can draw is that the simulation is recreating the floating mass peak quite well. The data is never more than 3% higher or lower than the simulation and a flat-line fit is really darn close to one. Couple this with the Zgg comparison and I think we can say that we are simulating the EMC (and SMD) response correctly, at least as it concerns reconstructing neural pions between 5 - 20 GeV/c. Of particular interest is the Zgg comparisons at relatively higher Pt, as then the distance between the two reconstructed photons is smaller than the size of one tower, and we rely on the SMD to correctly identify the daughter photons.

Concerning the High Mass Tail on the Pion Peak

At the PWG meeting a few weeks ago, people expressed concerns about the high-mass tail from the pion peak that, for higher PT bins, is underestimated by the pion peak simulation. This is shown (in the most egregious case) below (see especially, the left-hand plot between .2 and .5):

It is clear, at least at high-pt that the aggregate MC model does not fully reproduce this 'bleeding edge'. Naturally the question arises of how sensitive the cross section measurement is to this tail. One way to probe the sensitivity is to vary the invariant mass cut around the peak position. For the preliminary result, I used a 3 sigma cut calculated by fitting the mass peak to a gaussian. I can also calculate the cross section using a 2 sigma and 4 sigma cut. These cuts are shown below...

This plot shows a closeup of the pion peak for each of the 9 bins, with vertical lines showing the three different mass windows. For most bins, the 2 sigma cut completely excludes the tails and the 4 sigma cut includes a large portion of the tails.

I can then compare the different windows to the nominal window and the smaller (larger) windows.

Bin	% diff 4 sig	% diff 2 sig
1	2.4	1.4
2	1.6	0.5
3	3.3	1.7
4	6.2	3.0
5	5.6	4.5
6	10.6	4.8
7	10.3	5.3
8	13.5	2.3
9	10.1	0.62

The largest % difference is 13.5%, in the 8th bin. For the most part the higher pt bins can be off by ~10% for a large mass window.

Cut Variation Tests

Goal:

To test the stability of the cross section measurement to changes in the analysis cuts and, if necessary, assign a systematic uncertainty for cut variations.

Justification:

At many points in the measurements I make cuts on the data. For example, I place a maximum Zgg cut of 0.7 on all pion candidates. These cuts are motivated by our understanding of the detector and underlying physics, but the specific location of each cut is somewhat arbitrary. The cuts could move by some small amount and still be well-motivated. The measurement should be relatively stable with respect to changing analysis cuts. To test this, I take the three most important cuts, Zgg, inv. mass window, and z vertex, and vary them by some small amount in either direction. I then repeat the analysis from start to finish to discern the effect (if any) these cut changes have on the final cross section. The procedure is similar to that used to test the BEMC energy scale systematic (seen here)

Plots:

Instead of showing the cross section measurement for each individual cut change, I will plot the results in what I hope is a more illuminating way. Below is nine plots, each one representing a Pt bin in my analysis. The Pt range in GeV/c is shown in the upper right hand corner. Plotted on each canvas are the (Delta Sigma)/(Sigma) points for each of the cut variations (see key below.) Also note the solid lines indicating the statistical error of the nominal cross section measurement in that bin.

KEY:

point x position Cut Change

2 Invariant Mass Window of plus/minus 4sigma (nominal is 3sigma)

3 Zgg - 10%

4 Z vertex cut + 10%

7 Invariant Mass Window of plus/minus 2sigma (nominal is 3sigma)

8 Zgg cut - 10%

9 Z vertex - 10%

Broadly speaking, the points on the left side of the dashed-dotted line at 5.5 are cut changes that ought to increase raw yield and cuts on the right side ought to decrease raw yield. Of course an increase (decrease) in raw yield does not always translate into an increase (decrease) in cross section because the raw yields are corrected. Note that for most bins the effect for all cut changes is small (on the same order as the statistical uncertainty.) Other systematics (BEMC energy scale and yield extraction) dominate the uncertainty.

Data MC comparison

Data and MC comparison for pion mass peak position..

The left side shows the individual mass peak positions for Data (black) and MC (red). The MC points trend a touch higher than the data point. On the right is (Data-MC)/MC. The right side plot is fit to a pol0, the value of the fit is shown to be -1.01%.

Data Sets

For the cross section analysis I am using a number of Monte Carlo samples along with one data set. The details of each of these sets can be found below:

Pion enhanced, QCD 2->2 sample (full event): aka "T2 Platinum":

This MC sample was produced at Tier 2 by Mike Betancourt for me. It consists of ~200,000 events in each of following partonic pt bins {5-7, 7-9, 9-11, 11-15, 15-25, 25-35} GeV. The events were pre-filtered at the pythia level so every event contains a pythia-level pion with the following kinematic cuts: Pt > 4 GeV, -1.2 < particle eta < 1.2. The code to generate the trees lives on /star/u/ahoffman/T2_maker and is complied in SL08c. The MuDsts and .geant files are archived at HPSS. The trees themselves (600 files) live on /star/institutions/mit/ahoffman/Pi0Analysis/T2_platinum/Trees/. The following parameters were set in the analysis macro:

db1->SetDateTime(20060522,93000);
//variables for the trig simulator
    int flagMC=1; // 0== off
    int useEemc=1; // 0== off
    int useBemc=1; // 0== off
    int useL2=1; // 0== off
    int L2ConfigYear = 2006; //possible 2008
    int bemcConfig=2; // enum: kOnline=1, kOffline, kExpert
    int playConfig=100; // jan:100_199
    int emcEveDump=0; // extrating raw EMC data in a custom format
    char *eemcSetupPath="/afs/rhic.bnl.gov/star/users/kocolosk/public/StarTrigSimuSetup/";
//Settings for Emc simu maker:
    int controlval = 2;
    emcSim->setCalibSpread(kBarrelEmcTowerId,0.15);
    emcSim->setCalibOffset(kBarrelEmcTowerId,0.);
    emcSim->setCalibSpread(kBarrelSmdEtaStripId,0.25);
    emcSim->setCalibOffset(kBarrelSmdEtaStripId,0.0);
    emcSim->setMaximumAdc(kBarrelSmdEtaStripId,700);
    emcSim->setMaximumAdcSpread(kBarrelSmdEtaStripId,70);
    emcSim->setCalibSpread(kBarrelSmdPhiStripId,0.25);
    emcSim->setCalibOffset(kBarrelSmdPhiStripId,0.0);
    emcSim->setMaximumAdc(kBarrelSmdPhiStripId,700);
    emcSim->setMaximumAdcSpread(kBarrelSmdPhiStripId,70);
pre_ecl->SetClusterConditions("bemc", 4, .4, .05, 0.02, kFALSE);
pre_ecl->SetClusterConditions("bsmde", 5, 0.4,0.005, 0.1,kFALSE);
pre_ecl->SetClusterConditions("bsmdp", 5, 0.4,0.005, 0.1,kFALSE);

As of now, only events which pass the software trigger conditions for trigger 137611 (HTTP L2gamma) or 117001 (mb) are saved. These events are weighted properly using Mike B's custom weight calculator for his filtered events. That code can be found /star/u/ahoffman/BetanWeightCalc/

Single Particle Monte Carlo Sets

I have three separate single particle MC samples, single pion, single eta, and single gamma. These were produced using the code located at /star/u/ahoffman/SingleParticle_platinum/. The starsim, bfc, and treemaking code is all there. The .MuDsts and .geant files that result from the bfc jobs are run through a treemaker similar to that for the full pythia monte carlo. These samples are used to estimate the background shapes in in the diphoton invariant mass spectrum. The single gamma sample, for example, is used to model the 'low mass' background, as pion candidates are found from split clusters. The following cuts were set in the macro:

db1->SetDateTime(20060522,93000);
//settings for emc simu maker:
    int controlval = 2;
    emcSim->setCalibSpread(kBarrelEmcTowerId,0.15);
    emcSim->setCalibOffset(kBarrelEmcTowerId,0.);
    emcSim->setCalibSpread(kBarrelSmdEtaStripId,0.25);
    emcSim->setCalibOffset(kBarrelSmdEtaStripId,0.0);
    emcSim->setMaximumAdc(kBarrelSmdEtaStripId,700);
    emcSim->setMaximumAdcSpread(kBarrelSmdEtaStripId,70);
    emcSim->setCalibSpread(kBarrelSmdPhiStripId,0.25);
    emcSim->setCalibOffset(kBarrelSmdPhiStripId,0.0);
    emcSim->setMaximumAdc(kBarrelSmdPhiStripId,700);
    emcSim->setMaximumAdcSpread(kBarrelSmdPhiStripId,70);

    pre_ecl->SetClusterConditions("bemc", 4, .4, .05, 0.02, kFALSE);
    pre_ecl->SetClusterConditions("bsmde", 5, 0.4,0.005, 0.1,kFALSE);
    pre_ecl->SetClusterConditions("bsmdp", 5, 0.4,0.005, 0.1,kFALSE);
    pre_ecl->SetClusterConditions("bprs", 1, 500., 500., 501., kFALSE);

One important difference to note is that these events are not held to the simulated trigger standard. Instead, I only choose events (offline) that have pion candidates with Pt above the trigger threshold. Essentially this assumes that the trigger efficiency is perfect for such events. Obviously this is not true, but in my analysis these samples are used primarily to estimate the background line shapes. The single pion events are not weighted. The other two samples are weighted according to the funcional form given by the PHENIX cross section measurements.

Data

This analysis is only concerned with run 6 data, and only data that satisfies the HTTP L2gamma trigger (online and software.) I restrict myself to good runs between 7139017 and 7143025, as it is the longest period of run 6 with relatively stable tower and SMD status tables. Parts of the barrell are missing in this run range, and this will be accounted for in a geometric acceptance correction, but I believe that the stability of the status tables is more important that having a 100% live detector. Using a stable run range will cut down on the systematic error of the measurement which, given previous measurements, will be larger than the statistical error. The data was produced by Murad using my StSkimPionMaker (which can be found in StSpinPool) as part of the SpinAnalysisChain. The output trees are located at /star/institutions/mit/ahoffman/Pi0Analysis/Murads_Production_2_08/. The following parameters were made in the macro:

//Get TriggerMaker
StTriggerSimuMaker *simuTrig = new StTriggerSimuMaker("StarTrigSimu");
simuTrig->setMC(false); // must be before individual detectors, to be passed
simuTrig->useBbc();
simuTrig->useBemc();
simuTrig->bemc->setConfig(StBemcTriggerSimu::kOffline);
StGenericL2Emulator* simL2Mk = new StL2_2006EmulatorMaker;
assert(simL2Mk);
simL2Mk->setSetupPath("/afs/rhic.bnl.gov/star/users/kocolosk/public/StarTrigSimuSetup/");
simL2Mk->setOutPath(outPath);
simuTrig->useL2(simL2Mk);
//Tight cuts (esp. SMD)
pre_ecl->SetClusterConditions("bemc", 4, 0.4, 0.05, 0.02, kFALSE);
pre_ecl->SetClusterConditions("bsmde", 5, 0.4,0.005, 0.1,kFALSE);
pre_ecl->SetClusterConditions("bsmdp", 5, 0.4,0.005, 0.1,kFALSE);
pre_ecl->SetClusterConditions("bprs", 1, 500., 500., 501., kFALSE);

As of now, only events which pass either HTTP L2gamma (online and software) or MB triggers are saved. Only the L2g triggered events are used in the analysis. All other cuts are made offline and will be listed in individual analysis sections to follow.

Mixed Event Background:

I should note that I also make use of a 'mixed event' sample that is made by taking photons from different (real data) events and mixing them to form pion candidates. This sample is used to model the combinatoric background as described here.

Data/Filtered Pythia Inv. Mass Distributions

Details

Data Vs. T2 Platinum (see here)

Cuts:

Events pass L2gamma software trigger and (for data) online trigger.
candidate pt > 5.5
charged track veto
at least one good strip in each smd plane
Z vertex found
| Z vertex | < 60 cm
Zgg < .7
| particle eta | < .7

Bins:

9 pt bins, with boundries {5.5, 6., 6.75, 7.5, 8.25, 9.25, 11., 13., 16., 21.}

plots:

All MC plots are normalized. They are scaled to match the number of counts in the data between M = .08 and M = .25 GeV/c^2 (the nominal signal region.)

The above plots shows the data/mc comparison for the first four bins (the bin number is in the upper right corner.) The MC peak position and width track relatively well to the data. In the data, there is an excess of 'low mass' background events. This is expected as the MC is filtered for high pt pion events and thus the background will be artificially suppressed compared to the signal.

The above plot shows the last five bins along with the total comparison for all bins (lower right)

The Data (black) and MC (red) are fit to separate gaussians between .1 and .2 GeV/c^2. The mean masses for each are plotted below. Apologies for the lack of axis labels. The x-axis is pion pt and the y-axis is mean mass of that pt bin. The errors on the points are the errors on the fits. I would take the last bin with a grain of salt considering the stats.

The blue line is the pdg mean mass of the pion. As you can see, the MC tracks the data very well, recreating the rising mass peak.

Fully Corrected Yields

Ok, now we're cooking. Most of the ingredients are in place. We have our background subtracted raw yields. We have our generalized correction factor to account for inefficiencies in trigger, reconstruction, etc. Now, it's time to take a first look at a cross section. At a high level, we'll be starting with the raw yields, and applying a number of corrections for geometrical acceptance, efficiencies, etc. to recreate the true distribution. The formula for the invariant differential cross section:

Where:

Pt = Average Pt in a bin (as an aside, all points are plotted at this value)

N_raw = background subtracted raw yields

delta_pt = bin width in pt

delta_eta = 1.4 (= size of pseudorapidity range -.7 to .7)

C_trig+reco = Trigger + Reconstruction Efficiency (Generalized) Correcton Factor

Gamma/Gamma = branching fraction for Pi⁰ -> gamma gamma (=98.8%)

L = Luminosity

After applying all of these corrections, we arrive at the cross-section below.

The a) panel shows the invariant cross section along with 2 NLO pQCD predictions (based on DSS and KKP FFs.) The b) panel shows the relative statistical errors on the cross section. Panel c) shows the (data-NLO)/NLO for both pQCD predictions as well as for predictions from DSS at two different factorization scales. The points are placed at the average Pt for a bin. As you can see on in panel c) the measured cross section agrees well with theory for both DSS and KKP FFs.

Jigsaw Fits (1st try)

Goal:

Properly model the signal and background to the invariant mass plots using four single particle MC sets normalized to fit the data. Further, subtract out background contributions to arrive at a raw yield for each pt bin.

Details:

Data Vs. Single Particle MC (see here)

Cuts:

Data events pass L2gamma trigger and L2gamma software trigger
Cand Pt > 5.5
Charged Track Veto
At least one good strip in each SMD plane
Z Vertex found and |vtx| < 60.
Zgg < .7

Bins:

9 pt bins, with boundries {5.5, 6., 6.5, 7., 7.75., 9., 11.5, 13.5, 16., 21.}

Plots:

Above is a plot of the invariant mass distributions for the 9 pt bins used in this analysis. The black crosses represent the data (with errors.) The four colored histograms show the invariant mass distributions of pion candidates found in single pion MC (purple), Single photon MC (red), single eta MC (blue) and mixed-event MC (green). The four distributions are simultaneously fit to the data.

The above plot shows a data/MC comparison, where the red MC curve is the sum of the four single particle curves shown in plot 1. As you can see (especially in bins 3-7) the single particle MC seems to be underestimating the width of the pion peak, especially on the high mass side. The MC peak is too narrow. I think this can be explained by two effects. First, I am overestimating the energy precision of the towers and SMDs. The width of the mass peak is directly related to the energy resolution of the towers and SMD. I think this is telling us that the simulated resolution is too precise. Also, there is the issue of jet background, which is not present in these simulations and would tend to add small amounts of energy to each photon (thus increasing the mass and the pt of the pion candidate.)

Conclusions:

Obviously this MC parameterization is not quite good enough to compare to the data. I want to go back and remake the MC distributions with more smearing in the energy resolution, and perhaps with a small pt-dependent term to simulate the jet background.

Jigsaw Fits (2nd try)

Following up from this post concerning the modeling of the invariant mass distribution using different monte carlo samples for the signal and three sources of background, I respun through the modeling algorithm with an added smearing to the masses. The procedure is outlined below.

Goal:

Details:

Data Vs. Single Particle MC (see here)

Cuts:

Data events pass L2gamma trigger and L2gamma software trigger
Cand Pt > 5.5
Charged Track Veto
At least one good strip in each SMD plane
Z Vertex found and |vtx| < 60.
Zgg < .7
| particle eta | < .7

Bins:

9 pt bins, with boundries {5.5, 6., 6.5, 7., 7.75., 9., 11.5, 13.5, 16., 21.}

Smear:

The mass and of the single pions are smeared by sampling from a pt dependent normal distribution of the form N(1.005*Pt,.04). Mass = smear*smear*old_mass. This is supposed to mimic not only the detector resolution, but also the artificial increase in photon energy resultant from excess jet background at higher Pt.

Obviously this is not the correct way to do this sort of smearing; it should be done at the BEMC level in the simulation, but this is a rapid way to test out assumption that smearing will better recreate the mass peak.

Plots:

The above plot shows a data/MC comparison, where the red MC curve is the sum of the four single particle curves shown in plot 1. As you can see (especially in bins 3-7) the single particle MC much better recreates the mass peak with the smearing. It is not perfect, but compared to the original test, at least by eye, it looks better.

Conclusions:

I think this test supports the conclusions that the BEMC response for single pion simulation was too precise originally. Extra Pt dependent smearing should be added into the BEMC tower response.

Jigsaw Inv Mass Plots (final)

As noted here and here, the pion peak is difficult to model using single-particle MC. In single particle studies, the pion inv mass peak is reconstructed to narrow. Instead of trying to manipulate the single particle MC to conform to the data (using smearing techniques) I decided instead to model the pion peak using identified pions from the filtered pythia MC sample, where identified in this context means the reconstructed pion is less than .01 (in eta-phi space) away from a thrown pythia pion. As seen here, the mean and width of the peaks from the filtered pythia set match the data extremely well.

Goal:

Properly model the signal and background to the invariant mass plots using single particle MC background shapes and identified pion peak shapes from filtered pythia MC normalized to fit the data. Further, to subtract out the background on a bin by bin basis to arrive at a raw yield for each pt bin.

Details:

Data Vs. Single Particle MC (see here) and Identified Pions from filtered pythia MC (see here)

Cuts:

Data events pass L2gamma trigger and L2gamma software trigger
filtered pythia MC events pass L2gamma software trigger
Cand Pt > 5.5 GeV
Charged Track Veto.
At least one good strip in each SMD plane
Z Vertex found and |vtx| < 60 cm.
Zgg < 0.7
|particle eta| < 0.7

Bins:

9 pt bins, with boundries {5.5, 6., 6.5, 7., 7.75., 9., 11.5, 13.5, 16., 21.}

Plots:

Above is a plot of the invariant mass distributions for the 9 pt bins used in this analysis. The black crosses represent the data (with errors.) The four colored histograms show the invariant mass distributions of pion candidates found in identified pions from filtered pythia MC (purple,) Single photon MC (red,) single eta MC (blue,) and mixed-event MC (green.) The four distributions are simultaneously fit to the data.

The above plot shows a data/MC comparison, where the red MC curve is the sum of the four single particle curves shown in plot 1. As you can see (especially in bins 3-7) the identified pion spectrum from filtered pythia MC much better recreates the mass peak than single particle (compare here.)

The above plot shows the diphoton invariant mass spectrum for each of the 9 pt bins after the background shapes have been subtracted off. To calculate the background subtracted raw yields, these peaks are integrated from mean - 3sigma to mean +3sigma of a gaussian fit to the peak.

Preliminary Cross Section Plot

The proposed final version of the cross section plot would look like this.

Preliminary Presentation (8/27/09)

The link below has the presentation for preliminary cross section result.

Reconstruction and Trigger Efficiency Correction Factor

Now that I have my raw pion spectrum (see here) I need to proceed in transforming those raw counts into a cross section measurement. The first step in this process is calculating a correction factor that accounts for inefficiencies in the trigger and reconstruction algorithm. I will calculate this correction factor using the filtered, full-pythia MC sample. To first order the correction factor C = N_reco(Pt)/N_true(Pt) where N_reco = the number of pions found with our reconstruction algorithm and trigger, binned in Pt, after all cuts have been applied, and N_true is the true number of pions in the pythia record within the usable detector volume that should have fired the trigger. Note that C is not strictly a reconstruction efficiency. It represents a convolution of efficiencies in the reconstruction algorithm, trigger, acceptance, finite detector resolution, bin migration, merging and splitting effects.

Goal:

Calculate generalized correction factor.

Data Sets Used:

T2 Platinum MC (see here.) Previous studies (here and here) show that this MC sample very reasonably mimics the real data, especially within the pion mass peak.

Cuts:

filtered pythia MC events pass L2gamma software trigger
Reco/True Pt > 5.5 GeV
Charged Track Veto.
At least one good strip in each SMD plane
Reco Z Vertex found and |vtx| < 60 cm.
Reco Zgg < 0.7
Reco/True |particle eta| < 0.7 (*)

Bins:

9 pt bins, with boundries {5.5, 6., 6.5, 7., 7.75., 9., 11.5, 13.5, 16., 21.}

Plots:

The above plot shows the generalized correction factor N_reco(Pt)/N_true(Pt). N_reco is the number of reconstructed pions, where pions are found and reconstructed using the exact same procedure as is done for real data. The events in the MC sample are properly weighted.

We would like to check the applicability of a correction factor calculated similarly to the one above. To do this I split the filtered MC sample into two separate samples. One of these (MCd) I treat as data and the other (MCt) I treat as MC. I run the MCd sample through normal pion reconstruction and extract a raw yield spectrum. Then from the MCd sample I calculate a correction factor similar to the one above. I then apply the correction factor to the MCd raw yield distribution. The results are below.

The black squares are the raw yields of the 'data' set as a function of Pt. The red squares are the true pt distribution of pythia pions. The blue squares are the fully corrected yields obtained by applying the correction factor to the black points. As you can see, after applying the correction factor to the reconstructed 'data' the true distribution is obtained.

Special Attention Ought to be Paid to Zgg

The collaboration has concerns about the SMD, and not without reason. They, they SMDs, have been notoriously hard to understand and model. And the SMD is central to my analysis so I need to be able to trust that I understand it. To this end, I am using a comparison of Zgg in Data and MC to claim that I understand the SMDs well enough. The SMDs are mainly used in my analysis to split the energy of tower clusters into single photons. Zgg is a measurement of this splitting. This effect is exaggerated at higher values of Pt, where the two photons from a single pion are most likely to fall in a single pion.

Below is nine plots of Data (black) Vs MC (red) for my 9 pt bins in my cross section analysis. The MC is normalized (over all pt bins) to the integral of the data.

As you can see, the data and MC line up very well within statistics. The last bin should be taken with multiple grains of salt considering the number of entries in the hists. This is the justification I offer for using simulated SMD response in calculating correction factors and background shapes to compare to the data.

Tables

Yield Extraction and Correction systematic

Goal:

To calculate a combined systematic error for the yield extraction (i.e. background subtraction) and the correction factor, and to do so in a way that allows me to properly combine systematic uncertainties and statistical uncertaintiees in a meaningful way.

Method:

as shown here, my background-subtracted raw yields are calculated using what I call the jigsaw method. I model the background shapes using simulated data (both single particle and full pythia.) These shapes are simultaneously fit to the data and then subtracted from the data leaving a pure pion peak. This peak is integrated over to find the raw yeild in any particular bin. Obviously this method is succeptible to uncertainty, especially in the normailzation of the background shapes to the data. This normalization determines how many counts are subtracted from the data and has a direct influence on the final counts.

Previous analyses have taken a maximum-extent approach to this problem. The raw yields are calculated using some extreme scenario such as assuming no background at all or fitting the background to a polynomial. A major problem with this method is that these extreme scenarios are improbable. They sample only the largest outliers of whatever underlying distribution the background actually is. Further, these systematic uncertainties are then quoted as on equal footing with statistial uncertainties arising from gaussian processes and constituting 1 sigma errors. Thus, the systematics are vastly overestimated which leads to a large overall uncertainty and a weaker measurement. This problem is compounded when separate maximum extent errors are calculated for related variables (such as yield extraction and correction factor) and then added together in quadrature. We ought to be able to do better.

As stated above the end result of the Jigsaw method is a set of scaling parameters for each of the background shapes. The shapes are scaled by these parameters and then subtracted away. If the scaling parameters are wrong, the final yield will be wrong. Fortunately, the fitting procedure provides not only a scale for the shape but an uncertainty on that scale. So we know, how likely the scale is to be wrong. Instead of picking an outlying scenario (e.g. all scaling factors = 0) we can calculate the yields with a range of scaling factors sampled from an underlying gaussian probability distribution with a mean of the nominal scaling value and a width of the error on that scaling factor. By sampling enough points, we can build up a probability distribution for the measured point (which should also be gaussian) and take a 1 sigma error on that distribution. This error will not only be more accurate but will be on equal footing with the statistical error of the measured point.

Of course the final cross section value is a convolution of the background subtracted raw yields and the generalized correction factor. We need to vary the fitting paramaters for both at the same time to obtain an accurate estimation of the error on the final value. When we do this we get distributions for the final cross sections on a bin by bin basis. See below

Bins:

9 pt bins, with boundries {5.5, 6., 6.5, 7., 7.75., 9., 11.5, 13.5, 16., 21.}

Plots:

The above shows the bin by bin cross section values after 10,000 iterations of the sampling procedure described above. The systematic error for yield extraction + correction factor can be taken to be the width/mean of the above gaussian fits.

The Bin by bin relative systematic errors are as follows

Bin Rel. Sys.

1 14.7%

2 8.2%

3 10.2%

4 9.6%

5 12.3%

6 11.6%

7 12.0%

8 13.2%

9 25.0%

previous measurements of the systematic uncertainties for these two contributions (when added in quadrature) yield an average systematic of ~20%. As you can see, this method produces substantially reduced uncertainties in most bins in a fashion that is (as I argue above) more robust than previous methods.

DIS 2008 proceedings

Here are my proceedings for DIS 2008

DNP 2007 Inclusive Hadron Talk

Here are the powerpoint and pdf versions of my slides for DNP.

Pions in Jets study

Jet Pion Study

Procedure

In making the below plots, I employed the Spin Analysis Trees recently put together. I used all of the jets available, in all the jet triggers, assuming that the jets in the analysis tree are "physics" jets. I used pions from the L2-gamma and L2-gamma-test triggers. I used a run if it passed both my pion QA and Murad/Steve's Jet QA. That is, I used only those runs that were in both my golden run list and Jim Sowinski's golden run list.

First I look for any event with a pion found with -0.3 < Pion Eta < 0.3. I then loop over every jet in that event looking for any jet that is less then 0.7 away in Sqrt((delta Eta)² + (delta phi)²). I look in such a limited Eta range for the pion so as to avoid any edge effects. I then make a histogram of the ratio of pion Pt to Jet Pt and take the mean value of that histogram. This mean value is plotted below.

Currently this includes pions only from BBC timebinds 7,8, and 9. But I will soon add timebin 6 as well.

Plots

The above plot shows the ratio of Pion Pt to Jet Pt as a function of the Pion's Pt. It looks like as Pt gets higher and higher the ratio tends towards a fixed value between 75% and 80%

This histogram shows the distance in eta and phi between pion and associated jets. As you can see the RMS in the Eta-plane (x axis) is 0.064 and the RMS in the Phi-plane (y axis) is 0.055, which corresponds to the values from run 5.

Run QA

Update: 3/11/2008

All of my analysis leading up to the preliminary result uses one runlist which consist of the golden runlist from below plus about a dozen runs from the jet group's list. I added to my list any run that the jet group deemed good as long as the run log browser for that run didn't show any problems. the final runlist can be found here.

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Update: 6/19/2007

At the collaboration meeting in Berkeley some of out collaborators pointed out some flaws in my initial QA, most notably that I did not include BBC time bin #6. I have now redone my run QA and asymmetry calculations including this timebin. This results in a new 'golden' runlist which can be found below.

golden runlist (6/19)

All pages below should be up to date.
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My first task is to determine a preliminary runlist over which I will run my analyses. Using Murad's jet QA as a guide (found here), I have looked at a number of different event criteria. At the moment, I am focusing on the second longitudinal period runs only. The data set is comprised of about 390 runs, with about 420,000 triggered events (including production and test L2-G triggers.) Each run number has an index (for easier plotting;) this index can be found here. I am also (for now) restricting myself to one trigger,:

HTTP-L2Gamma (137611)
Min Bias (117001, for reference)

Some book keeping stats for this running period

Run Range: 7131043 - 7156040
Fill Range: 7847 - 7957
Days: 131 - 156 (May 11 - June 5, 2006)

My results can be found on the pages below.

Preliminary Run List

As of 2/1/07 I have created a preliminary list of 'good' runs. If a run failed any of my QA tests for either HT trigger, I excluded it from this list. I tried to be as discriminating as possible for this list. I feel it will be easier to go back and add runs if they are later determined to be good. The preliminary list includes 302 runs, and can be seen by clicking below.

Preliminary List.

Furthermore, using the run log browser I have checked all of my excluded runs to see if there is an obvious reason why it shouldn't be included. Many of the runs did indeed have glaring reasons, but not all of the runs. A summary of this check can be found here.

Also, since I plan on gathering polarization information on a fill by fill basis, I have created a list of relevent fills which can be found here. Or if you would like a complete list of run numbers with associated fills, that can be found here.

Eta v. Phi

The above plots show 2-d histograms of eta and phi for pion candidates. The plot on the top shows the candidates from the production L2-G trigger. The plot on the bottom shows the eta and phi distribution for candidates from the L2-G 'test' trigger. As of May 2007, I am not using these to exclude any runs. I used Frank's pion trees to make these plots, imposing the following cuts on any pion candidate:

Energy > 0.1 GeV
PT > 5.0 GeV
Asymmetry < .8
Mass between .08 and .25 GeV
No charged track association.
BBC Timebin = 7,8 or 9.

The above plot shows histograms of the eta and phi positions of individual photons (hits). Again the top plot shows photons from events from the production L2-G trigger while the bottom shows photons from the 'test' L2-G trigger. Note the lack of structure in these plots compared to the candidate plots. This is due to the SMD, which is used in making the top plots (i.e. a candidate needs to have good SMD information) and unused in the bottom plots (a hit does not reference the SMD.) The status tables for different fill periods can be found at the bottom of this page. You can see that there are some gaps in the smd which could be responsible for the structure in the Candidate plot.

Event Ratios

This plot shows, as a function of run number, the number of L2-G triggered events divided by the number of minbias triggers (w/ prescale.) This shows all the runs, before any outliers are removed.

This plot above shows a the histograms of the top plots. There is some subtlety here (as you have probably noticed) in that the data needs to be analyzed in four sets. The largest set (corresponding to the top left histogram) is all of the L2-G triggers from the 'production' level data, which consists of all the runs after ~100. The other three sets are all subsets of the 'test' status (i.e. the first ~100 runs) and reflect changes in the prescale and threshold levels. The characteristics from each subset are noted below.

Runs 3 -14 (top right):
Initial test thresholds
Prescale = 1

Runs 23 - 44 (lower left):
Higher thresholds (?)
Prescale = 2

Runs 45 - 93 (lower right):
Lower thresholds
Prescale = 1

Runs 93+ (top left):
Final thresholds (5.2 GeV)
Prescale = 1

The above plot shows the event ratio after outliers have been removed. To identify these outliers I took a four-sigma cut around the mean for each of the four histograms shown above, and removed any runs that fell outside this cut. The list of outlying runs and thier characteristics can be found here.

Pion Number

On the left are two plots showing the number of pions per triggered event (L2-G trigger) as a function of run index. The top plot is for all of the runs when the L2-G trigger had production status, while the bottom plot exhibits the runs for which the L2-G trigger had 'test' status (about the first 100 runs or so.) On the right are histograms of these two plots. To remove outliers from the runlist, I made a two-sigma cut around the mean of these histograms and removed any run that fell outside this range. Below are plots showing pion number per triggered event after the outlying runs have been removed. Note the change in scale from the original. A list of excluded runs and thier properties can be found here.

As you can see, there is some funny structre in the above plot. It's almost bi(or tri)-modal, with the mean number of pions per trigger jumping between .06 and .08 (or even .1). I think this is a consequence of the SMD. See below.

The above is a plot of the number of triggered photons that have GOOD SMD status normalized by the total number of triggered photons, as a function of run number. While this is crude, it gives an approximate measure of the percent of active SMD strips in the barell for each run. As you can see, the peaks and vallys in this plot mirror the peaks and vallys in pion yield above. Since the SMD is not required to satisfy the trigger but IS required to reconstruct a pion, we would expect that the pion reconstruction efficiency would decrease as the number of active SMD strips decreases. Indeed this is what appears to be happening.

I used Frank's pion trees to make these plots, imposing the following cuts on any pion candidate:

Photon E_t > 0.1 GeV
PT > 5.2 GeV
Asymmetry < .8
Mass between .08 and .25 GeV
No charged track association.
BBC Timebin = 6, 7, 8, or 9.
'Good' SMD status.

Pion Yields

currently not in use

Single Spin Asymmetries

Run By Run

Runs are indexed from the beginning of the long-2 period. For reference, take a look at this file.

By Transverse Momentum

Tower and SMD info

This is a plot of the tower status as a function of relative day (since the start of the second longitudinal period.) The 4800 towers are on the Y-Axis and Days are on the X axis. A dot means that that tower had a status other than good during that time period.

Tower Status Geometry

The below plots show the status as a function of phi and eta for different days representing different status periods. The title of the histogram refers to the relative day in the second longitudinal period (e.g. hGeomStatus_5 is for the fifth day of long-2.) The x and y axes correspond to detector phi and eta, and any space that is not white is considered a 'bad' tower. As you can see, for most of the barell for most of the time, most of the towers are good. Only for specific day ranges are there chunks of the barrel missing.

SMD Status

These two plots were made by Priscilla and they show the SMD status flags for SMD strips vs. Fill number. In these plots the strip number is plotted along the X-axis and the fill index number is plotted along the Y-Axis. Priscilla's fill index list can be found here. The important range for my particular analysis (i.e. the second longitudinal range) runs between fill indices 41 - 74. For my analysis I ignore any SMD strip that has a status other than good (== 0).

SMD Status Geometry

The plots below show the smd status as a function of geometry. The location of the strip in eta-phi space (w/ phi on the x axis and eta on the y axis.) You can see the eta and phi strip planes for four different fills (representative of different configurations) 7855, 7891, 7921, and 7949. Using Priscilla's iindexing these fills correspond to fills 46, 57, 68, and 72 in the above plots. We can see that these runs mark the beginning of major changes in the status tables, and each plot below represents a relativley stable running period. For my analysis I consider any area that is not light blue (i.e. any area with status other than 1) to be bad. Please note the difference between the SMD geometry plots and the BTOW geometry plots, namely, in the SMD plots whitespace represents 'bad' strips, whereas in the BTOW plots whitespace represents 'good' towers. All plots below were made by Priscilla.

Z Vertex

The above left plot shows average z vertex as a function of run index for the L2-G trigger. The upper plot shows all runs for which the L2-G trigger had production status, while the lower plot shows the first ~100 runs, for which the L2-G trigger had test status. The above right plot shows a histogram of the points on the above left plot. These plots show the average z vertex for all the events in a run, that is, they are not limited to pion candidates (as in some of the other QA measures.)

this plot above shows the average z vertex of L2-G events as a function of run index, with outlying runs removed. To identify outying runs, I took a four-sigma cut around the mean of each histogram (showed top right) and excluded any run for which the average z vertex fell outside this cut. Currently, I separate the 'test' from 'production' runs for analysis of outlyers. It would not be hard to combine them if this is deemed preferable. A list of the excluded runs, with thier average z verex can be found here.

SPIN 2008 Talk for Neutral Pions

Hi all-

My Spin 2008 talk and proceedings can be found below.

Update:

v2 reflects updates based on comments from SPIN pwg and others

Spin PWG Meeting (2/22/07)

Statistics for Late Longitudinal Running

~ 400 runs (7132001 - 7156028)
~ 6.2 million events
~ 170K Neutral Pions for HTTP L2 Gamma trigger
~ 80K Neutral Pions for HT2 Trigger

Trigger Ratios

This plot shows, for the four triggers (HTTPL2, HT2, JP1, MB) the number of triggers registered normalized by the number of
minbias triggers registered, as a function of run index.

Pions per Event

This plot shows the average number of neutral pions in an triggered event as a function of run number. For my purposes a
neutral pion consists of a reconstructed pion (using Frank's trees) that has the following cuts:

Energy of each photon > 0.1 GeV
Pt > 0.5 GeV
Asymmetry < 0.8
Mass between 0.08 and 0.25 GeV
No charged track associated w/ the photons

Invariant Mass by Pt Bin

This plot shows the two-photon invariant mass spectrum for the HTTPL2-Gamma trigger, which is my most abundant and cleanest trigger. The mass
spectra are split into 1 GeV Pt bins, with the exception of the last one which is for everything with Pt more than 11 GeV.

Parsed Run List

Using the QA data shown on plots above (with more detail in my run QA page) I have come up with a preliminary 'good run list.' Basically I took the above event ratio and number ratio plots (along with a similar one for average z-vertex) and made a 4 sigma cut on either side the mean value. Any run with a value that fell outside this cut for either of the high tower triggers was excluded from my preliminary run list (right now the JP1 plots have no impact and are presented for curiosity sake.) Every run that passed these three tests was labeled 'good' and included in my list. My list and the associated run indexes can be found here. The list contains 333 runs.

A_LL Checklist

The following is a checklist of 'to do' tasks toward a preliminary A_LL measurement.

Relative luminosity data for the late-longitudinal runs
Polarization data (note, I have the numbers for the fast-offline CNI measurements and am refining the data for release.)
Decisions on details (Pt binning, final mass window, etc.)
Montecarlo studies
Background studies
Studies of other systematic effects.
Other?

Any and all suggestions would be greatly appreciated. Thank you.

Spin PWG meeting (2/12/09)

The PDF for my slides is linked below.

Towards a Preliminary A_LL

Links for the 2006 Neutral Pion A_LL analysis

The numbered links below are in 'chronological' type order (i.e. more or less the order in which they should be looked at.) The 'dotted' links below the line are a side-effect in Drupal that lists all daughter pages in alphebetical order. They are the exact same as the numbered links and probably should just be ignored.

1. Run List QA

2. Polarization Files

3. Relative Luminosity Files

4. Cuts etc.

5. Pt Dependent Mass

6. Invariant Mass Distribution

7. Yield Extraction

8. A_LL and Statistical Errors

9. Systematic Errors

10. Sanity Checks

11. DIS Presentation

A_LL

The measurement of A_LL for inclusive neutral pion production is seen below along with statistical error bars and a systematic error band. This asymmetry was calculated using a class developed by Adam Kocoloski (see here for .cpp file and here for .h file.) Errors bars are statistical only and propagated by ROOT. The gray band below the points represents the systematic uncertainties as outlined here. The relative luminosities are calculated on a run by run basis from Tai's raw scalar counts. Each BBC timebin is treated seperately. The theory curves are GRSV predictions from 2005. These will change slightly for 2006.

Bin A_LL (x10^-2)

1 0.80 +/- 1.15

2 0.58 +/- 1.36

3 2.03 +/- 1.89

4 -0.84 +/- 3.06

Cuts and Parameters

Here I will detail the some general information about my analysis; topics that aren't substantial enough to warrant their own page but need to be documented. I will try to be as thorough as I can.

Data

Run 6 PP Long 2 Only.

Run Range: 7131043 - 7156040

Fill Range: 7847 - 7957

Days: 131 - 156 (May 11 - June 5, 2006)

HTTP L2gamma triggered data (IDs 5, 137611)

Run List

Trees using StSkimPionMaker (found in StRoot/StSpinPool/) and Murad's spin analysis chain macro

Trees located in /star/institutions/mit/ahoffman/Pi0Analysis/Murads_Production_1_08/

Cluster Finding Conditions

Detector Seed Add

BEMC 0.4 GeV 0.05 GeV

SMDe/p 0.4 GeV 0.005 GeV

Pion Finding Cuts

Event passes online and software trigger for L2gamma

Energy asymmetry (Zgg) <= 0.8

Charged track veto (no photon can have a charged track pointing to its tower)

BBC timebins 6,7,8,9 (in lieu of a z vertex cut)

At least one good SMD strip exists in each plane

-0.95 <= eta <= .95

Z vertex found

Pt Bins for A_LL

All reconstructed pion candidates are separated by Pt into four bins:

5.20 - 6.75

6.75 - 8.25

8.25 - 10.5

10.5 - 16.0

Simulation

Full pythia (5 to 45 GeV/c in partonic Pt, weighted)

Single pion, photon, and eta simulations (2 - 25 GeV/c in particle Pt, unweighted)

EMC simulator settings:

15% Tower Spread

25% SMD Spread

SMD Max ADC: 700

SMD Max ADC spread: 70

(see this link for explanation of SMD Max ADC parameters)

Four representative timestamps used:

20060516, 062000 (40%)

20060525, 190000 (25%)

20060530, 113500 (25%)

20060603, 130500 (10%)

Full pythia trees located in /star/institutions/mit/ahoffman/Pi0Analysis/MC_Production_2_08/

Single particle trees located in /star/institutions/mit/ahoffman/Pi0Analysis/single_particle_simulations/

DIS 2008 Presentation

Below you will find a link to a draft my DIS 2008 presentation.

Energy Subtracted A_LL Calculation

For my single particle Monte Carlo studies, I argued (here) that I needed to add a small amount of energy to each reconstructed photon to better match the data. This small addition of energy brings the simulation mass distributions into better alignment with the mass distributions in the data. I did not, however, subtract this small bit of energy from reconstructed (data) pions. This affects A_LL in that pion counts will migrate to lower Pt bins and some will also exit the low end of the mass windown (or enter the high end.) So I calculated A_LL after subtracting out the 'extra' energy from each photon. The plot below shows the original A_LL measurement in black and the new measurement in red.

The values from both histograms are as follows:

Bin black (orig) red (new)

1 .0080 .0095

2 .0058 .0092

3 .0203 .0196

4 -.0084 -.0069

So things do not change too much. I'm not sure which way to go with this one. My gut tells me to leave the data alone (don't correct it) and assign a systematic to account for our lack of knowledge of the 'true' energy of the photons. The error would be the difference between the two plots, that is:

Bin Sys. Error (x10^-3)

1 1.5

2 3.4

3 0.7

4 1.5

Integral Delta G Study

Using Werner's code I calculated A_LL for pi0 production over my eta range for 15 different integral values of Delta G. Below I plot the measured A_LL points and all of those curves.

I then calculated chi^2 for each of the curves. Below is the chi^2 vs. integral delta g.

The red line shoes minimum chi^2 + 1. For my points, GRSV-Std exhibits the lowest chi^2, but GRSV-Min is close. My data indicates a small positive integral value of delta g in the measured x range.

Invariant Mass Distribution

The two-photon invariant mass distribution can be roughly broken up into four pieces, seen below^*.

Fig. 1

The black histogram is the invariant mass of all pion candidates (photon pairs) with pt in the given range. I simulate each of the four pieces in a slightly different way. My goal is to understand each individual piece of the puzzle and then to add all of them together to recreate the mass distribution. This will ensure that I properly understand the backgrounds and other systematic errors associated with each piece. To understand how I simulate each piece, click on the links below.

1. Pion Peak

2. Eta Peak

3. Low Mass Background

4. Combinatoric Background.

Once all of the four pieces are properly simulated they are combined to best fit the data. The individual shapes of are not changed but the overall amplitude of each piece is varied until the chisquared for the fit is minimized. Below are plots for the individual bins. Each plot contains four subplots that show, clockwise from upper left, the four individual peices properly normalized but not added; the four pieces added together and compared to data (in black); the ratio of data/simulatio histogramed for the bin; and a data simulation comparison with error bars on both plots.

Bin 1: 5.2 - 6.75 GeV/c

Bin 2: 6.75 - 8.25 GeV/c

Bin 3: 8.25 - 10.5

Bin 4: 10.5 - 16.0

Below there is a table containing the normalization factors for each of the pieces for each of the bins as well as the total integrated counts from each of the four pieces (rounded to the nearest whole count.)

Table 1: Normalization factors and total counts

bin	low norm.	low integral	pion norm.	pion integral	eta norm.	eta integral	mixed norm.	mixed integral
1	121.3	9727	146.4	75103	20.91	5290	0.723	44580
2	77.34	4467	77.81	51783	20.86	6175	0.658	34471
3	40.13	3899	29.41	23687	12.93	6581	1.02	18630
4	5.373	1464	5.225	8532	2.693	3054	0.521	6276

Table 2 below contains, for each source of background, the total number of counts in the mass window and the background fraction [background/(signal+background)].

Table 2: Background counts and Background Fraction

Bin	Low Counts	Low B.F (%)	Eta Counts	Eta B.F (%)	Mixed Counts	Mixed B.F (%)
1	2899	3.60	1212	1.50	4708	5.84
2	2205	3.96	917	1.65	3318	5.96
3	2661	9.29	633	2.21	1507	5.26
4	858	8.56	170	1.70	591	5.89

_{* Note: An astute observer might notice that the histogram in the top figure, for hPtBin2, does not exactly match the hPtBin2 histogram from the middle of the page (Bin 2.) The histogram from the middle of the page (Bin 2) is the correct one. Fig. 1 includes eta from [-1,1] and thus there are more total counts; it is shown only for modeling purposes.}

Combinatoric Background

The last piece of the invariant mass distribution is the combinatoric background. This is the result of combining two non-daughter photons into a pion candidate. Since each photon in an event is mixed with each other photon in an attempt to find true pions, we will find many of these combinatoric candidates. Below is a slide from a recent presentation describing the source of this background and how it is modeled.

---------------------------------------

-------------------------------------------

As it says above we take photons from different events, rotate them so as to line up the jet axes from the two events, and then combine them to pion candidates. We can then run the regular pion finder over the events and plot out the mass distribution from these candidates. The result can be seen below.

These distributions will be later combined with the other pieces and normalized to the data. For those who are interested in the errors on these plots please see below.

Eta Peak

I treat the eta peak in a similar way as the pion peak. I throw single etas, flat in Pt from 2 - 25, and reconstruct the two-photon invariant mass distribution for the results. The thrown etas are weighed according to the PHENIX cross-section as outlined here. The mass distributions for the four pt bins can be seen below. (I apologize for the poor labeling, the x-axis is Mass [GeV/c^2] and the y-axis is counts.) Don't worry about the scale (y-axis.) That is a consequence of the weighting. The absolute scale will later be set by normalizing to the data.

These plots will later be combined with other simulations and normalized to the data. The shape will not change. For those interested in the errors, that can be seen below.

Low Mass Background

The low mass background is the result of single photons being artifically split by the detector (specifically the SMD.) The SMD fails in it's clustering algorithm and one photon is reconstructed as two, which, by definition, comprises a pion candidate. These will show up with smaller invariant masses than true pions. Below is a slide from a recent presentation that explaines this in more detail and with pictures.

----------------------------------

We can reproduce this background by looking at singly thrown photons and trying to find those that are artificially split by the clustering algorithm. Indeed when we do this, we find that a small fraction (sub 1%) do indeed get split. We can then plot the invariant mass of these pion candidats. The results can be seen below. (x-axis is mass in GeV/c^2.)

These mass distributions will later be combined with other pieces and normalized to the data. For those interested in the errors on these histograms please see below.

Pion Peak

To study the pion peak section of the invariant mass distribution I looked at single pion simulations. The pions were thrown with pt from 2 - 25 GeV/c flat and were reconstructed using the cuts and parameters described in the cuts, etc. page. The mass of each reconstructed pion is corrected by adding a small amount of energy to each photon (as outlined here.) After this correction the peak of the reconstructed mass distribution is aligned with the peak of the data. The mass distributions from the four bins can be seen below.

Later, these peaks will be normalized, along with the other pieces, to the data. However, the shape will not change.

If you are interested in seeing the errors on the above plots, I reproduce those below.

Polarization

I am using the final polarization numbers from run 6, released by A. Bazilevsky to the spin group on December 4, 2007. The files can be found below.

Pt Dependent Mass

The two-photon invariant mass is given (in the lab frame) by

M = Sqrt(2E₁E₂(1 - Cos(theta)))

where E₁ and E₂ are the energies of the two photons and theta is the angle between those photons. For every real photon we should measure ~135 MeV, the rest mass of the pi0. Of course, the detectors have finite resolution and there is some uncertainty in our measurement of each of the three quantities above, so we should end up measuring some spread around 135 MeV.

But it is not that simple. We do not see a simple spread around the true pion mass. Instead, we see some pt dependence in the mean reconstructed mass.

The above left plot shows the two-photon invariant mass distribution separated into 1 GeV bins. The pion peak region (between ~.1 and .2 GeV) has been fit with a gaussian. The mean of each of those gaussians has been plotted in the above right as a function of Pt. Obviously the mean mass is increasing with Pt. This effect is not particularly well understood. It's possible that the higher the Pt of the pion, the closer together the two photons will be in the detector and the more likely it is that some of the energy from one photon will get shifted to the other photon in reconstruction. This artificially increases the opening angle and thus artificially increases the invariant mass. Which is essentially to say that this is a detector effect and should be reproducible in simulation. Indeed...

The above plot overlays (in red) the exact same measurement made with full-pythia monte carlo. The same behavior is exhibited in the simulation. Linear fits to the data and MC yield very similar results...

-- M = 0.1134 + 0.0034*Pt (data)

-- M = 0.1159 + 0.0032*Pt (simulation)

If we repeat this study using single-particle simulations, however, we find some thing slightly different.

-- M = 0.1045 + 0.0033*Pt

So even in single-particle simulation we still see the characteristic rise in mean reconstructed mass. (This is consistent with the detector-effect explanation, which would be present in single-particle simulation.) However, the offset (intercept) of the linear fit is different. These reconstructed pions are 'missing' ~11 MeV. This is probably the effect of jet background, where other 'stuff' in a jet get mixed in with the two decay photons and slightly boost their energies, leading to an overall increase in measured mass.

The upshot of this study is that we need to correct any single-particle simulations by adding a slight amount of extra energy to each photon.

Relative Luminosity

For my relative luminosity calculations I use Tai's relative luminosity file that was released on June 14th, 2007.

Luminosity File

I read this using the StTamuRelLum class, which, given a run number and BBC timebin, reads in the raw scalar values for each spinbit. Each event is assigned raw scalar counts for each spinbit and every event from the same run with the same timebin should have the same scalar counts assigned. When it comes time to calculate my asymmetry, the relative luminosity is calculated from these scalar counts.

Run List

Below you will find the runlist I used for all of the studies leading up to a preliminary result. For a more detailed look at how I arrived at this runlist please see my run QA page.

Golden Run List

Sanity Checks

Below there are links to various 'sanity' type checks that I have performed to make sure that certain quantities behave as they should

Mass Windows

The nominal mass window was chosen 'by eye' to maximize the number of pion candidates extracted while minimizing the backgrounds (see yield extraction page.) I wanted to check to see how this choice of mass window would affect the measurement of A_^LL. To this end, A_LL was calculated for two other mass windows, one narrower than the nominal window (.12 - .2 GeV) and one wider than the nominal window (.01 - .3 Gev). The results are plotted below where the nominal window points are in black, the narrow window points are in blue and the wide window points are in red. There is no evidence to indicate anything more than statistical fluctuations. No systematic error is assigned for this effect.

Revised Eta Systematic

After some discussion in the spin pwg meeting and on the spin list, it appears I have been vastly overestimating my eta systematic as I was not properly weighing my thrown single etas. I reanalyzed my single eta MC sample using weights and I found that the background contribution from etas underneath my pion peak is negligible. Thus I will not assign a systematic error eta background. The details of the analysis are as follows. First I needed to assign weights to the single etas in my simulation. I calculated these weights based on the published cross section of PP -> eta + X by the PHENIX collaboration (nucl-ex 06110066.) These points are plotted below. Of course, the PHENIX cross section on reaches to Pt = 11 GeV/c and my measurement reaches to 16 GeV/c. So I need to extrapolate from the PHENIX points out to higher points in Pt. To do this I fit the PHENIX data to a function of the form Y = A*(1 + (Pt)/(Po))^-n. The function, with the parameters A = 19.38, P0 = 1.832 and n = 10.63, well describes the available data.

I then caluclate the (properly weighted) two-photon invariant mass distribution and calculate the number of etas underneath the pion peak. The eta mass distributions are normalized to the data along with the other simulations. As expected, this background fraction falls to ~zero. More specifically, there was less than ten counts in the signal reigon for all four Pt bins. Even considering a large background asymmetry (~20%) this becomes a negligable addition to the total systematic error. The plots below show the normalized eta mass peaks (in blue) along with the data (in black.) As you can see, the blue peaks do not reach into the signal reigion.

Unfortunately, the statistics are not as good, as I have weighted-out many of the counts. I think that the stats are good enough to show that Etas do not contribute to the background at any significant level. For the final result I think I would want to spend more time studying both single particle etas and etas from full pythia.

I should also note that for this study, I did not have to 'correct' the mass of these etas by adding a slight amount of energy to each photon. At first I did do this correction and found that the mass peaks wound up not lining up with the data, When I removed the correction, I found the peaks to better represent the data.

In summary: I will no longer be assigning a systematic from eta contamination, as the background fraction is of order 0.01% and any effect the would have on the asymmetry would be negligible.

Single Spin Asymmetries

The plots below show the single spin asymmetries (SSA) for the blue and yellow beams, as a function of run index. These histograms are then fit with flat lines. The SSA's are consistent with zero.

Systematics

We need to worry about a number of systematic effects that may change our measurement of A_LL. These effects can be broadly separated into two groups: backgrounds and non backgrounds. The table below summarizes these systematic errors. A detailed explanation of each effect can be found by clicking on the name of the effect in the table.

Systematic Effect	value {binwise} (x10^-3)
Low Mass Background	{1.0; 1.1; 3.8; 1.0}
Combinatoric Background	{1.0; 0.86; 1.6; .03}
Photon energy Uncertainty	{1.5; 3.4; 0.7; 1.5}
Non Longitudinal Components^*	0.94
Relative Luminosity^*	0.03
Total^**	{2.3; 3.8; 4.3; 2.0}

* These numbers were taken from the Jet Group

** Quadrature sum of all systematic errors

Alternate Calculation of Eta Systematic

At the moment I am calculating the systematic error on A_LL from the presence of Etas in the signal reigon using theoretical predictions to estimate A_LL^eta. The study below is an attempt to see how things would change if I instead calculated this systematic from the measured A_LL^eta. Perhaps, also, this would be a good place to flesh out some of my original motivation for using theory predictions.

First order of buisness is getting A_LL^eta. Since this was done in a quick and dirty fashion, I have simply taken all of my pion candidates from my pion analysis and moved my mass window from the nominal pion cuts to a new window for etas. For the first three bins this window is .45 GeV/c² < mass < .65 GeV/c² and for the fourth bin this is .5 GeV/c² < mass < .7 GeV/c². Here is a plot of A_LL^eta, I apologize for the poor labeling.

I guess my first reason for not wanting to use this method is that, unlike the other forms of background, the Etas A_LL ought to be Pt dependent. So I would need to calculate the systematic on a bin-by-bin basis, and now I start to get confused because the errors on these points are so large. Should I use the nominal values? Nominal values plus one sigma? I'm not sure.

For this study I made the decison to use A_LL^true as the values, where A_LL^true is calculated from the above A_LL using the following formula:

A_LL^M = (A_LL^T + C*A_LL^Bkgd)/(1 + C)

Remembering that a large portion the counts in the Eta mass window are from the combinatoric background. I then calculate the background fraction or comtamination factor for this background (sorry I don't have a good plot for this) and get the following values

Bin Bkg fraction

1 77.2%

2 60.9%

3 44.6%

4 32.6%

Plugging and chugging, I get the following values for A_LL^true:

Bin A_LL^T

1 .0298

2 .0289

3 -.0081

4 .175

Using these values of ALL to calculate the systematic on A_LL^pion I get the following values:

Bin Delta_A_LL^pion (x10^-3)

1 .33

2 .38

3 .63

4 3.1

Carl was right that these values would not be too significant compared to the quad. sum of the other systematics, except for final bin which becomes the dominant systematic. In the end, I would not be opposed to using these values for my systematic *if* the people think that they tell a more consistent story or are easier to justify/explain than using the theory curves. (i.e. if they represent a more accurate description of the systematic.)

Combinatoric Systematic

For the combinatoric background systematic we first estimate the background contribution (or contamination factor) to the signal reigon. That is we integrate our simulated background to discern the precentage of the signal yield that is due to background counts. The plots below show, for each of the four bins, the background fraction underneath the singal peak. The background (simulation) is in green and the signal (data) is in black and the background that falls in the signal reigon is filled-in with green.

The background fractions for the bins are

Bin 1: 6.1%

Bin 2: 6.1%

Bin 3: 5.7%

Bin 4: 6.2%

Then I consider how much this background fraction could affect my measured asymmetry. So I need to meausre the asymmetry in the high-mass reigon. I do this, taking the mass window to be 1.2 to 2.0 GeV/c². I do not expect this asymmetry to be Pt-dependant, so I fit the asymmetry with a flat line and take this to be the asymmetry of the background, regardless of Pt. The plot below shows this asymmetry and fit.

Finally, I calculate the systematic error Delta_A_LL = A_LL^True - A_LL^Measured where:

A_LL^M = (A_LL^T + C*A_LL^Bkgd)/(1 + C)

And C is the background fraction. This yields in the end as a systematic error (x10^-3):

Bin 1: 1.0

Bin 2: 0.9

Bin 3: 1.6

Bin 4: 0.03

Eta Systematic

For the eta background systematic we first estimate the background contribution (or contamination factor) to the signal reigon. That is we integrate our simulated background to discern the precentage of the signal yield that is due to background counts. The plots below show, for each of the four bins, the background fraction underneath the singal peak. The background (simulation) is in blue and the signal (data) is in black and the background that falls in the signal reigon is filled-in with. Below that is the same four plots but blown up to show the contamination.

Here we blow up these plots

The background fractions for the bins are

Bin 1: 1.50%

Bin 2: 1.65%

Bin 3: 2.21%

Bin 4: 1.70%

Then I consider how much this background fraction could affect my measured asymmetry. So I need to measure the asymmetry in the etas. So instead of measuring the asymmetry in the etas I will use a theoretic prediction for A_LL. From GRSV standard and GRSV min I approximate that the size of A_LL in my Pt range to be between 2 - 4%.

I stole this plot from C. Aidala's presentation at DNP in Fall 2007. Since GRSV-Max is ruled out by the 2006 jet result I restrict myself only to min, standard and zero. For a conservative estimate on the systematic, I should pick, for each Pt bin, the theory curve that maximizes the distance between the measured and theoretical asymmetries. Unfortunately, at this time I do not have predictions for Pt above 8, so I must extrapolate from this plot to higher bins. For the first two bins this maximum distance would correspond to GRSV standard (A_LL^bg ~ 0.02). For the third bin, this would correspond to GRSV = 0 (A_LL^bg ~ 0.). For the fourth bin (which has a negative measured asymmetry) I extrapolate GRSV standard to ~4% and use this as my background A_LL.

Systematic (x10^-3)

Bin 1: 0.18

Bin 2: 0.23

Bin 3: 0.43

Bin 4: 0.82

Low Mass Systematic

For the low mass background systematic we first estimate the background contribution (or contamination factor) to the signal reigon. That is we integrate our simulated background to discern the precentage of the signal yield that is due to background counts. The plots below show, for each of the four bins, the background fraction underneath the singal peak. The background (simulation) is in red and the signal (data) is in black and the background that falls in the signal reigon is filled-in with red.

The background fractions for the bins are

Bin 1: 3.5%

Bin 2: 4.2%

Bin 3: 9.3%

Bin 4: 8.3%

Then I consider how much this background fraction could affect my measured asymmetry. So I need to meausre the asymmetry in the low-mass reigon. I do this, taking the mass window to be 0 to 0.7 GeV/c². I do not expect this asymmetry to be Pt-dependant, so I fit the asymmetry with a flat line and take this to be the asymmetry of the background, regardless of Pt. The plot below shows this asymmetry and fit.

Finally, I calculate the systematic error Delta_A_LL = A_LL^True - A_LL^Measured where:

A_LL^M = (A_LL^T + C*A_LL^Bkgd)/(1 + C)

And C is the background fraction. This yields in the end as a systematic error (x10^-3):

Bin 1: 1.0

Bin 2: 1.1

Bin 3: 3.7

Bin 4: 1.1

Yield Extraction

After all the pion candidates have been found and all the cuts applied, we need to extract the number of pions in each bin (in each spin state for A_LL.) To do this we simply count the number of pion candidates in a nominal mass window. I chose the mass window to try to maximize the signal region and cut out as much background as possible. For the first three Pt bins, this window is from .08 - .25 GeV/c² and in the last bin the window is from .1 - .3 GeV/c². The window for the last Pt bin is shifted mostly to cut out more of the low mass background and to capture more pions with higher than average reconstructed mass. The windows can be seen below for bins 2 and 4.

These Pt bins are broken down by spin state, and the individual yields are reported below.

Spin Sorted Yields
Bin	uu	du	ud	dd	Total
1	20209	20088	19961	20231	80489
2	14088	14057	13645	13825	55615
3	7224	7080	7107	7204	28615
4	2505	2470	2552	2491	10018

Tower By Tower Pion Peaks

Calibration

I am trying to calibrate the BEMC using neutral pions. As of now I am only using the L2 Gamma trigger for the second longitudinal running period, although I plan on incorporating the transverse data as well since the calibration should be the same for the two running periods. The procedure, so far, is listed below. Items 1 - 4 have been done for the long2 data set and are not difficult. Items 5+ I have not done yet, nor am I certain if they are the correct way to proceed.

1. Find all pion candidates using Frank's finder.
2. Calculate the mass for each di-photon pair.
3. Find the tower struck by each photon in the candidate and fill that tower's histogram with the invariant mass of the pion candidate. Note that if the two photons strike two different towers, both towers are filled with the mass of one pion.
4. Fit the towers with a gaussian + linear function and extract the location of the peak (see below.)

* * *

5. Using the mass peaks, calculation a correction factor for each tower, and apply this correction factor to the tower response (gains? I'm not sure.)
6. Repeat until desired level of precision is reached.

Plots

Good Tower:

Poor Statistics (Hope to improve w/ Transverse):

Poor Fit:

Spiked Fit (Chi^2 = 60/32 if you can't read it.)

A .pdf with all 4800 towers, and their fits can be found here. It is about 50 MB.

Run 6 Relative Luminosity (Tai Sakuma)

Along with the spin sorted yields and the polarization, the relative luminosity is important piece of the spin asymmetries and is measured by BBC and ZDC. The relative luminosity is primarily determined from the BBC data. The systematic uncertainty is determined from the comparison between BBC and ZDC.

2006 Relative luminosity preliminary release

Run 8 trigger planning (Jim Sowinski)

here are some presentations from the run 8 trigger planning.

Run 9

Collect documentation on run 9 here.

Goals

at 500 GeV 10pb^-1 at 50% pol. FOM=P^2L=2.5pb^-1 End of run summary
at 200 GeV 50pb^-1 at 60% pol. FOM=P^4l=6.5pb^-1 Track progress from Jamie here or online page

Statement of priorities for run

CAD projections for run 9

BTOW calibration thread and pages

Early polarimeter commisioning see links in March 5 pwg meeting.

Link to magnet cooldown.

JP threshold ADC->GeV and initial guesses link

500 GeV trigger list

500 GeV run ends ~noon on Monday April 13 2009. FOM collected 1.4pb-1 vs 2.5pb-1 goal (email).

First overnights at 200 GeV April 18 and 19.

Report on run to convenors 4/21/09

5/27/09 STAR magnet field reversed from direction previously.

Phil Pile planning meeting postings

Jamies trigger summary

Run 9 Preparation and Jobs List

Green = complete Red = currently critical

Run 9 Preparation Tasks (includes commissioning at startup
- ZDC SMD hardware preparation Whitten
  - planning and analysis - Hal, Valpo, LBL
  - install DAQ and testing - Aihong
  - Calibration and monitoring - Ramon
- VPD as polarimeter - LBL
- Trigger plan
  - L0, L2 optimization - TAMU
  - FMS
  - coordination with other PWGs
- L0 trigger monitor from trigger data - Pibero
- DSM replacements - Hank
- Analyze 2008 data to:
  - check triggers etc.
  - BSMD operation, gains
  - influence of low mass on gammas
- L2 algos
  - coordination - Corliss
  - endcap gamma-jet - Ilya
  - barrel gamm-jet - Betancourt
  - dijet algo - Page
  - Peds - Page
  - monitoring web pages - Betancourt
  - Upsilon - Haidong Liu
  - Ws - Seele
  - Calibration - Corliss
  - High energy - Corliss
- BEMC gain equalization
  - Eta = 1 and -1
  - E/W balance and phi dep in east
- BSMD, BPRS readout upgrade and data compression
  - Hardware - Visser, Jacobs
  - hardware tests post delivery
  - zero suppression, peds
  - software infrastructure and monitor - Leight
- BPRS Calibration and readiness
  - Characterization from previous runs - Balewski
  - FPGA fixes for cap ID - Visser
  - HV adjustments, fiber fixes
- Resolve hole in East Barrel trigger - Pibero
- Shielding additions at top of tunnel - Bill, Al, Jim
- Pplot upgrades to match changes - G. Webb, Tsai and Trentalange
  - Daq reader - core group
  - EMC - Walker
  - BSMD compression
  - trigger changes
  - DSM changes
- DSM wiring modifications and test
  - Rewire - Engelage, Gagliardi
  - tests - G. Webb, Grebenyuk
- High luminosity monitoring
  - L measurement planning - Hal
  - Scalers - Ernst, Jim, Joe, Hal, Dave, Chuck
  - L measurement implementation
- TPC monitor FGT prototype - ANL, Majka
- 500 GeV preparation
  - BSMD staturation studies
    - W program - Seele
    - gamma, pi0
  - Trigger differences from 200 GeV - Carl
  - Other
- ESMD->DDL data path and necessary software changes
- EEMC maintenance - Jacobs, Sowinski
- BEMC maintenance - Tsai, Trentalange
- FMS trigger work
  - Remap cabling for cluster triggering - Jim Drachanberg and/or
  - rebuild pre-shower system - Jim Drachanberg and/or

Run 9 Startup and During Run Tasks
- Scaler check out - Bridgeman
- Luminosity monitor checkout
- Real time monitor of lumi/local pol - LBL
- CDEV monitoring - Sowinski
- AGS polarimetery - Spinka, Underwood, Haixin Huang
- RHIC polarimetry - Spinka, Underwood, Qinghua/student
- RHIC polarimeter monitor and recording - Jones
- ZDC SMD tuneup
- Commission BEMC - Grebenyuk, Cendejas, Tsai, Trentalange
  - check calibration
  - check ADC timing
  - on call expert
- Commission EEMC - IU with help
  - check calibration
  - check ADC timing
  - on call expert
- Commission FMS
- Commission BBC
- Trigger setup
  - EMC trigger bit timing - Hank et al
  - Rates and thresholds scans and settings
  - confirmation good to go
  - L2 testing
  - L2 monitoring and updating
  - L2 on call expert

Preparation for analysis
- Luminosity -Jones
- runlist and QA - Page/Sowinski
  - Spin data base
  - Fill by fill spin patterns - Page
  - Bunch offsets and load DB
- Calorimeter Readiness
  - EEMC - IU
    - peds and status
    - claibrations
  - BEMC
    - peds and status
    - calibrations

Ongoing Service Work
- EEMC hardware maintenance - IU
- EEMC software coordinator
- EEMC calibrations
  - Sampling fraction MC studies
  - pi0, eta, electron, mips
- BEMC hardware maintenance - Tsai and Trentalange
- BEMC software coordinator
- BEMC calibrations - Tsai and Trentalange
  - pi0 - Renee
  - time dependence - Renee
- BEMC GEANT model refinements - Tsai and Trentalange
  - Other GEANT model refinements
  - TPC endcap and electronics - ANL
- FMS
  - Calibrations
  - run 9 - Zagreb
  - Root framework software - PSU,TAMU
  - FPD++ calib. - Poljak
- Common analysis software
  - Trigger simulator software - Fatemi, Grebenyuk?
  - L0 - Fersch
  - EEMC codes - Gagliardi/Huo
  - L2 ported to simulator incl. data base of params
  - Vertex finding and tracking
  - Jet finder
  - gamma maker
  - pi0 algos BEMC
  - pi0 algos EEMC
- FGT project - Simon, MIT, IU, KU, ANL, Valpo
- Tier 2 simulations - Btancourt

Run 9 commissioning plan

Run 9 500 GeV Commission plan (start of draft) Not necessarily time ordered
or prioritized

Establish collisions

Develop Minbias trigger
BBC
TCU
DSMs

BBC as lumi and polarimeter
Commission and test scalers
Look for analyzing power - need > 30%? pol

FMS triggers and commissioning

EMC commissioning

Need minbias trigger to start - pol irrelevant
Timing Scan
    Need stable beam background conditions - 2nd half of store?
    Guess 4 hrs? scan both at same time
    need EEMC and BEMC experts available
    1 day min. to produce and check
    Requires fast offline production of data on short time scale

HV adjustments and calibrations
    Needs timing scan complete and set
    Need minbias trigger - "low" backgrounds
    1hr of beam - 3 succesive days
    day of analysis of each run
    Need calo experts and analyzers

Polarimetry commissioning in addition to BBC
ZDC -
    signals into DAQ/trg
    establish trigger
    5-10M events with P>25%
    min 1 day of analysis - try to analyze from trigger data
    Suggest optimized trigger
    Longer run after seeing analysis of first data (~1 day)
    Needs detector and analysis experts
VPD
    signals into DAQ/trg
    signals at scalers
    runs parasiticaly
    Time to have answer few shifts of beam over few days?
    Needs detector and analysis experts

Commission high level calo triggers
Needs calibration steps done
set thresholds based on rate scans - needs 1/2 a store

check for an mask hot towers at L0 and L2
monitor and analyze for a day
check L2 algos
confirm all ready to go
need L2 experts, calo experts

Criteria to switch to longitudinal
Some polarimeter working well enough to measure transverse components
Sufficient data to evaluate alternate polarimeters

Priorities? Time ordering? What can go in parallel? What could
but is prevented from going in parallel due to overlap of people?

Run 9 triggering

Carl has prepared a trigger description for 200 GeV.

Analyses from the early years

(A) List of Physics Analysis Projects (obsolete)

Common Analysis Trees

Location

Interactive Mode

Batch Mode

What's Included?

Known Issues

Future Plans

Cuts Summary

Run 5

Run 6

Introduction at Spin PWG meeting - 5/10/07

Features

Included Analyses

Current Status

EEMC Direct Photon Studies (Pibero Djawotho, 2006-2008)

2006.07.31 First Look at SMD gamma/pi0 Discrimination

Simulation

Maximal side residual

xy distribution of SMD hits

Single peak characteristics

Fit function

Single event SMD response

Code

Documents

2006.08.04 Second Look at SMD gamma/pi0 Discrimination

Second Look at SMD gamma/pi0 Discrimination

Dataset

Results

SMD U and V Fits

Code

2006.08.06 Comparison between EEMC fast and slow simulator

Comparison between EEMC fast and slow simulator

EEMC Fast Simulator

EEMC Slow Simulator

2006.09.15 Fit Parameters

Fit Parameters

Fit Function

Simulation

Fit Widths

Data from 2005 pp200 EEMC HT 1 and 2 triggers

Data from 2005 pp200 electrons

2007.02.05 Reconstructed/Monte Carlo Photon Energy

Reconstructed/Monte Carlo Photon Energy

Fit to a constant

Fit to a line

Fit to a quadratic

Comparison between Weihong's and Pibero's results

Constant

Linear

Quadratic

Conclusion

References

2007.02.08 E_reco / E_mc vs. eta

E_reco / E_mc vs. eta

Legend

Jason's Monte Carlo

Will's Monte Carlo

2007.02.11 Reconstructed/Monte Carlo Muon Energy

Reconstructed/Monte Carlo Muon Energy

zvertex

pT vs. η

EMC

Etower/EMC vs. η

Ecluster/EMC vs. η

Etowertanh(eta) vs. eta

2007.02.15 160 GeV photons

160 GeV photons

2007.02.15 20 GeV photons

20 GeV photons

2007.02.15 80 GeV photons

80 GeV photons

2007.02.15 Reconstructed/Monte Carlo Electron Energy

Reconstructed/Monte Carlo Electron Energy

E=1 GeV

E=2 GeV

2007.02.19 10 GeV photons

10 GeV photons

2007.02.19 40 GeV photons

z_vertex

p_T vs. η

E_MC

E_tower/E_MC vs. η

E_cluster/E_MC vs. η

E_towertanh(eta) vs. eta

Partonic a_LL