Who  Institution  Data  Topic 

Jan Balewski  MIT  2009  W production 
Michael Betancourt  MIT  2009/6  prompt gamma midrap A_LL/cross sec. 
Alice Bridgeman  ANL  2009  EEMC gammas 
Thomas Burton  Birmingham  2006  Lambda trans. pol. 
Ramon Cendejas  UCLA/LBL  2008  dijet cross section 
Ross Corliss  MIT  2006/9  photons 1 
Pibero Djawotho  TAMU  2009  inclusive and dijet 
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 /midrapidity 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 dijet 
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  Midrapidity 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  20069  Forward gammajet 
David Staszak  UCLA  2006  Inclusive Jet A_LL 
Justin Stevens  IUCF  2010  TBD 
Naresh Subba  KSU  2006  NonPhotonic Elect. 1>eta>1.5 xsec 
Matthew Walker  MIT  2006/9  dijets cross section/A_LL 
Grant Webb  Kentucky  2009/6  midrap gamma or dijet/ UEvent 
Wei Zhou  Shandong U  hyperons  
WeiMing Zhang  KSU  2008  nonphotonic electrons EEMC A_LL 
root [1] TFile::Open("root://deltag5.lns.mit.edu//Volumes/scratch/common/run6/spinTree/spinAnalyses_7156028.tree.root"); 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 00013b6e72c31640a0e8e5243780beef 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>AddFriend("ConeJets"); root [4] spinTree>AddFriend("chargedPions"); root [5] spinTree>Draw("chargedPions.fE / ConeJets.fE","chargedPions.fE>0")
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")
//create a new reader StSpinTreeReader *reader = new StSpinTreeReader(); //add some files to analyze, one at a time or in a text file reader>selectDataset("$STAR/StRoot/StSpinPool/StSpinTree/datasets/run6_rcf.dataset"); //reader>selectFile("./spinAnalyses_6119039.tree.root");
Ok, so we created a new reader and told it we'd be using the files from Run 6 stored on RCF. You can also give it specfic filenames if you'd prefer, but there's really no reason to do so. //configure the branches you're interested in (default = true) reader>connectJets = true; reader>connectNeutralJets = false; reader>connectChargedPions = true; reader>connectBemcPions = true; reader>connectEemcPions = false; reader>connectBemcElectrons = false; //optionally filter events by run and trigger //reader>selectRunList("$STAR/StRoot/StSpinPool/StSpinTree/filters/run6_jets.runlist"); reader>selectRun(7143025); //select events that passed hardware OR software trigger for any trigger in list reader>selectTrigger(137221); reader>selectTrigger(137222); reader>selectTrigger(137611); reader>selectTrigger(137622); reader>selectTrigger(5); //we can change the OR to AND by doing reader>requireDidFire = true; reader>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. StJetSkimEvent *ev = reader>event(); TClonesArray *jets = reader>jets(); TClonesArray *chargedPions = reader>chargedPions(); TClonesArray *bemcPions = reader>bemcPions(); long entries = reader>GetEntries(); for(int i=0; i
Error in <tclass::new>: cannot create object of class StHelix</tclass::new>
Everything as a single pdf file (341 pages, 8.2Mb)
Pibero Djawotho
Indiana University
July 31, 2006
Simulation were done by Jason for the SVT review.
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). 
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.
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.
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:
Pibero Djawotho
Indiana University
August 4, 2006
The dataset used in this analysis is the 2005 p+p collision at √s=200 GeV with the endcap calorimeter hightower1 (eemcht1mb = 96251) and hightower2 (eemcht2mb = 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
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 
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:
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.
Sector #  SMDu σ_{core}  SMDu σ_{tail}  SMDv σ_{core}  SMDv 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 
In this sample, high tower triggers, eemcht1mb (96251) and eemcht2mb (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.
Here, I try to pick a representative sample of electrons from the 2005 pp200 dataset. The cuts used to pick out electrons are:
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.
This study is motivated by Weihong's photon energy loss study where an etadependence of reconstructed photon energy to generated photon energy in EEMC simulation was observed.
In this study, the etadependence 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.
The parameters from the fits are used to plot the fit functions for comparison between Weihong's and Pibero's results.
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 etadependence.
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.
10k muons thrown by Will with:
E=1 GeV 
E=2 GeV 
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):
For each photon energy, the ratio E_reco/E_MC vs. eta was plotted and fitted to the function p0+p1*(1eta), 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 etadependence 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 midregion 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 etadependence, 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 12bit 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 energydependence 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
The following
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 SMDu or SMDv plane. The widths of the narrow and wide Gaussians are determined from empirical fits of shower shape response in the EEMC from simulation.
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+1e7*x**5
The yaxis is integrated yield over the SMDu and SMDv plane, and the xaxis is the sum of the maximal sided residual of the SMDu and SMDv 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%.
The plot on the left below shows the efficiency of identifying photons over the pt range of 1030 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.
The plot below shows background rejection vs. signal efficiency for different energy ranges of the thrown gamma/pi0.
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=2040 GeV 

E=4060 GeV 

E=6080 GeV 

E=8090 GeV 

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.
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.
Jet 
Gamma 
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.
The class
computes the maximal sided residual of the SMD response in the u and vplane 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:
[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))
/star/institutions/iucf/balewski/prodOfficial06_muDst/
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
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.
/star/institutions/iucf/balewski/prodOfficial06_muDst/
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  eemcjp0etotmbL2jet  EEMC JP > th0 (32, 4 GeV) and ETOT > TH (109, 14 GeV), minbias condition, L2 Jet algorithm, reading out slow detectors, transverse running 
127271  eemcjp1mb  EEMC JP > th1 (49, 8 GeV) && mb, reading out slow detectors, transverse running 
127641  eemchttpmbl2gamma  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  bemcjp0etotmbL2jet  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  eemchttpmbl2gamma  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  eemcht2mbemul  EEMC HT > th2 (22, 5.0 GeV) && mb, reading out slow detectors, emulated in L2, transverse running, different threshold from 117262 
127271  eemcjp1mb  EEMC JP > th1 (49, 8 GeV) && mb, reading out slow detectors, transverse running 
127652  eemcjp0etotmbL2jet  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)
/star/institutions/iucf/hew/2006ppLongRuns/
Figure 2.1
Figure 2.2: The dominant trigger ids are:
137273  eemcjp1mb  EEMC JP > th1 (52, 8.7 GeV) && mb, reading out slow detectors, longitudinal running 2 
137641  eemchttpmbl2gamma  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  eemcht2mbemul  EEMC HT > th2 (22, 5.0 GeV) && mb, reading out slow detectors, emulated in L2, longitudinal running 2 
137222  bemcjp1mb  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 a tarball of the code used in this analysis.
f(x)=p0*(0.69*exp(0.5*((xp1)/0.87)**2)/(sqrt(2*pi)*0.87)+0.31*exp(0.5*((xp1)/3.3)**2)/(sqrt(2*pi)*3.3))
p0 = yield
p1 = centroid
21 electrons
99 electrons
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 2photon 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.
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.
A few histograms were added to the code:
Figure 1:
Data vs. MC mean ustrip
Figure 2:
Data vs. MC mean vstrip
Figure 3:
Data vs. MC ustrip sigma
Figure 4:
Data vs. MC vstrip sigma
Figure 5:
MC E
_{v}
vs. E
_{u}
Figure 6:
Data E
_{v}
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
or
.
In case you missed it, the first look is
. I processed
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()
.
I fit the diphoton invariant mass with two Gaussians, one for the pi0 peak (p0p2) and another one for the eta peak (p3p5) plus a quadratic for the background (p6p8). The Gaussian is of the form p0*exp(0.5*((xp1)/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 BreitWigner 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.
Pibero Djawotho Last updated at Tue Mar 4 16:21:13 EST 2008
The following is a revisited study of E_reco/E_MC for photons with the addition of the SMD energy to E_reco.
SectorVsRunNumber.png 10Feb2010 12:22 14K ShowerShapes.png 10Feb2010 12:22 17K chiSquareMC.png 10Feb2010 12:22 13K chiSquarePibero.png 10Feb2010 12:22 16K chiSquareWill.png 10Feb2010 12:22 16K chiSquareWillAndMC.png 10Feb2010 12:22 17K
The plots below show the conversion process before the Endcap. I look at prompt photons heading towards the Endcap from a MC gammajet sample with a partonic pT of 911 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.
This
file shows several shower shapes in a single plot for comparison:
These Shower Shapes are binned by:
(pre1==0&&pre2==0)
and (pre1>0pre2>0)
They are then fitted with a tripleGaussian 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]))
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
. 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]))
Partonic p_{T}=911 GeV  Partonic p_{T}=911 GeV 
During Run 6, the L2gamma 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 gammajets 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 gammajet 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_{gammajets} 
5  6.551E05  0.0992  30654 
6  3.075E05  0.1161  16840 
7  1.567E05  0.1150  8500 
8  8.654E06  0.1131  4617 
9  4.971E06  0.1223  2868 
10  2.953E06  0.1151  1603 
The p_{T} slope is exp(0.69*pT)=2^(pT)
, so the statistics are halved with each 1 GeV increase in p_{T}.
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 L2gamma 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  eemchttpmbl2gamma  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  eemchttpmbl2gamma  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  eemchttpmbl2gamma  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 figureofmerit (FOM) is calculated as FOM=Luminosity*P_{B}*P_{Y} for transverse runs and FOM=Luminosity*P_{B}^{2}*P_{Y}^{2} 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}]  Figureofmerit [nb^{1}] 
117641  7093102  7096017  118.88  11.89 
127641  7097009  7129065  3219.04  1099.43 
137641  7135050  7156028  4717.10  687.65 
For this study, only the trigger of longitudinal running 2 (137641) is used. As mentioned above, at level0, 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 level2, a high tower above 3.7 GeV and a 3x3 patch above 5.2 GeV in transverse energy is required to accept the event.
In addition to selecting events that were tagged online by the L2gamma trigger, the offline
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/
.
The gamma candidate is required to have no track pointing to any of its towers.
The gamma candidate is required to have 85% of the total transverse energy in a cone of radius 0.3 in etaphi space around the position of the gamma candidate. That is E
_{T}^{gamma}
/E
_{T}^{cone}
> 0.85 and R=√Δη
^{2}
+Δφ
^{2}
=0.3 is the cone radius.
The gamma candidate is matched to the best awayside 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/
.
Jan Balewski has an excellent writeup, 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 
L2gamma triggers  730128 
Endcap gamma candidates  723848 
Track isolation  246670 
EMC isolation  225400 
Awayside jet  99652 
SMD max sided residual  19281 
Barrelonly jet  15638 
Note the number of L2gamma triggers include only those events with a primary vertex and at least one gamma candidate (BEMC or EEMC).
After selecting Endcap gamma candidates out of L2gamma triggers, applying track and EMC isolation cuts, and matching the Endcap gamma candidate to an awayside 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 zaxis represents tower energy) and draw a circle of radius 0.3 around the gamma candidate and a circle of radius 0.7 around the awayside 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.
Note:
No cuts on residuals applied.
jet A_LL systematic possibility
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
_{B}
=P
_{Y}
=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 partonlevel 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).
Figure 1a  Figure 1b 
For this study, the gammajets Monte Carlo sample for all partonic p_{T} were used. As an example, the prompt photon processes for the partonic p_{T} bin 911 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 III (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 3.074E06 I I 14 f + fbar > g + gamma I 331 1337 I 4.930E07 I I 18 f + fbar > gamma + gamma I 2 8 I 1.941E09 I I 29 f + g > f + gamma I 1667 8019 I 2.579E06 I I 114 g + g > gamma + gamma I 0 1 I 1.191E10 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] 
34  0.0002962 
45  0.0000891 
57  0.0000494 
79  0.0000110 
911  0.00000314 
1115  0.00000149 
1525  0.000000317 
2535  0.00000000990 
3545  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 
PartonicKinematics.C 
DeltaG.C 
pythia6.tar.gz
from the ROOT site ftp://root.cern.ch/root/pythia6.tar.gz and unpack.
tar zxvf pythia6.tar.gzA directory
pythia6/
will be created and some files unpacked into it. Cd into it and compile the Pythia 6 interface to ROOT.
cd pythia6/ ./makePythia6.linuxFor more information, consult Installing ROOT from Source and skip to the section Pythia Event Generators.
tar zxvf pythia8108.tgzA directory
pythia8108/
will be created. Cd into it and follow the instructions in the README
file to build Pythia 8. Set the environment variables PYTHIA8
and PYTHIA8DATA
(preferably in /etc/profile.d/pythia8.sh
):
export PYTHIA8=$HOME/pythia8108 export PYTHIA8DATA=$PYTHIA8/xmldocRun configure with the option for sharedlibrary creation turned on.
./configure enableshared make
tar zxvf root_v5.20.00.source.tar.gz cd root/ ./configure linux withpythia6libdir=$HOME/pythia6 \ enablepythia8 \ withpythia8incdir=$PYTHIA8/include \ withpythia8libdir=$PYTHIA8/lib make make installSet the following environment variables (preferably in
/etc/profile.d/root.sh
):
export ROOTSYS=/usr/local/root export PATH=$PATH:$ROOTSYS/bin export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$ROOTSYS/lib:/usr/local/pythia6 export MANPATH=$MANPATH:$ROOTSYS/manYou should be good to go. Try running the following Pythia 6 and 8 sample macros:
root $ROOTSYS/tutorial/pythia/pythiaExample.C root $ROOTSYS/tutorial/pythia/pythia8.C
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
My blog pages, from first to last:
01/25: http://drupal.star.bnl.gov/STAR/blogentry/wleight/2008/jan/25/photonanalysisprogressweek1210812508. This post discusses the problem with the spike in secondary tracks at eta=1 in our singleparticle simulations.
01/28: http://drupal.star.bnl.gov/STAR/blogentry/wleight/2008/jan/28/furtherqaplots. 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/blogentry/wleight/2008/feb/01/moreqaplotstimeefficiencies. 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/blogentry/wleight/2008/feb/04/photonqaefficiencyplotserrorbars. This post adds error bars to the reconstruction efficiencies for the photon barrel sample.
02/05: http://drupal.star.bnl.gov/STAR/blogentry/wleight/2008/feb/05/firstclusteringplots. This post has the first clustering plots, for muons and gammas (barrel only), showing cluster energy, energyweighted 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/blogentry/wleight/2008/feb/12/preshowerplots. 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/blogentry/wleight/2008/feb/13/moreclusteringplots. 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/blogentry/wleight/2008/feb/19/clustertrackmatchingplots. This post investigates the clustertotrack 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/blogentry/wleight/2008/feb/21/morepreshowerplots. 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/blogentry/wleight/2008/feb/28/furtherpreshowerplotsnotcompletedyet. 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/blogentry/wleight/2008/mar/04/muonpreshowerplots. 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/blogentry/wleight/2008/mar/06/firstphysicscuts. 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/blogentry/wleight/2008/mar/18/smdqaplots. This post plots energyweighted 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/blogentry/wleight/2008/mar/28/smdclusteringplots. This post contains SMD clustering plots for barrel gamma and piminus samples.
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.
Here I show the invariant masses and corresponding widths I obtain using my cross section binning. These are compared to MC values.
The Method:
The results are shown in the two figures below.
Mass:
Width:
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).
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, GSC 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
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.
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.
Figure 2: Binbybin 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
Details on the A_LL result and the systematic studies:
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 
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.
Bin 1  Bin 2  Bin 3  Bin4  
relative luminosity  0.0009  0.0009  0.0009  0.0009 
nonlongitudinal 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 nonlongitudinal 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.
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.
False asymmetries (parityviolating 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.
Here is information regarding my analysis of transverse asymmetries in neutral strange particles using 2006 p + p TPC data. This followson from and expands upon the earlier analysis I did, which can still be found at star.bnl.gov/protected/strange/tpb/analysis/. Comments, questions, thingsyou'dliketoseedone and so forth are welcomed. I'll catalogue updates in my blog as I make them.
The links listed below are in 'analysisorder'; best to use these for navigation rather than the alphabetically listed links Drupal links below/in the sidebar.
email me at tpb@np.ph.bham.ac.uk
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 bemcjp0etotmbl2jet (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:
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:
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 (4bit) 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.
Figure 1: Mean event vertex z for each run. The red lines indicate the 3σ cut. 
I measure a double spin asymmetry defined as follows
Equation 1 
where N(anti)parallel indicates yields measured in one half of the detector when the beam polarisations are aligned (opposite) and P_{1} and P_{2} are the polarisations of the beams. Accounting for the relative luminosity, these yields are given by
Equation 2 
Equation 2 
where the arrows again indicate beam polarisations. Figures one and two show the fillbyfill measurement of A_{TT}, corrected by the beam polarisation, summed over all p_{T}.
Figure 1: K^{0}_{S} A_{TT} fillbyfill 
Figure 2: Λ A_{TT} fillbyfill 
The BetheBloch 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 πminus.
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 K^{0}_{S} because most background tracks are pions; the selection of an (anti)proton daughter rejects the majority of the background tracks.
Figure 1a: Invariant mass spectrum of V0 candidates under K0s hypothesis passing dE/dx cut 
Figure 1b: Invariant mass spectrum of V0 candidates under K0s hypothesis failing dE/dx cut 
Figure 2a: Invariant mass spectrum of V0 candidates under Λ hypothesis passing dE/dx cut 
Figure 2b: Invariant mass spectrum of V0 candidates under Λ hypothesis failing dE/dx cut 
Figure 3a: Invariant mass spectrum of V0 candidates under antiΛ hypothesis passing dE/dx cut 
Figure 3b: Invariant mass spectrum of V0 candidates under antiΛ hypothesis failing dE/dx cut 
Species  Pass (millions)  Fail (millions)  % pass 

K^{0}_{S}  95.5  48.9  66.2 % 
Λ  32.5  111.9  22.5 % 
antiΛ  11.8  132.5  8.2 % 
Table 1
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:
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 straightline 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 

K^{0}_{S}  1.0  1.2^{**}  0.5  0.0^{**}  2.0^{**} 
Λ  1.5  1.0  0.0^{**}  0.0^{**}  3.0 
antiΛ  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 microDST
Figure 1: Final K^{0}_{S} mass spectrum with all cuts applied. 
Figure 2: Final Λ mass spectrum with all cuts applied. 
Figure 3: Final antiΛ mass spectrum with all cuts applied. 
Equation one shows the crossformula used to calculate the single spin asymmetry.
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.
Equation 2 
Equation 3 
(in e.g. N(upUp), The first arrow refers to yellow beam polarisation, the second to blue beam.)
Beam polarisation  4bit spin bits  

Yellow  Blue  
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 fillbyfill asymmetries for each K^{0}_{S} and Λ for each beam are shown in figures one and two. AntiΛ results shall be forthcoming. An average asymmetry is calculated by performing a straight line χ^{2} fit through the fillbyfill 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 χ^{2} per degree of freedom and are consistent with zero within errors.
Figure 1a: K^{0}_{S} blue beam asymmetry 
Figure 1b: K^{0}_{S} yellow beam asymmetry 
Figure 2a: Λ blue beam asymmetry 
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.
Figure 3a: K^{0}_{S} p_{T}dependent blue beam A_{N} 
Figure 3b: K^{0}_{S} p_{T}dependent yellow beam A_{N} 
Figure 4a: Λ p_{T}dependent blue beam A_{N} 
Figure 4b: Λ p_{T}dependent yellow beam A_{N} 
I also calculate the asymmetry via an alternative method, making use of Tai Sakuma's relative luminosity work. The leftright 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 crossasymmetry section (equations 2 and 3). Here, the yields must be scaled by the appropriate relative luminosity, giving the following relations:
Equation 2 
Equation 3 
The relative luminosities R4, R5 and R6 are the ratios of luminosity for, respectively, upup, updown and downup bunches to that for downdown bunches. I record the particle yields for each polarisation permutation (i.e. spin bits) on a runbyrun basis, scale each by the appropriate relative luminosity for that run, then combine yields from all the runs in a given fill to give fillbyfill yields. These are then used to calculate a fillbyfill raw asymmetry, which is scaled by the beam polarisation. The figures below show the resultant fillbyfill 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.
Figure 1a: Blue beam asymmetry for K^{0}_{S} 
Figure 1b: Yellow beam asymmetry for K^{0}_{S} 
Figure 2a: Blue beam asymmetry for Λ 
Figure 2b: Yellow beam asymmetry for Λ 
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 

K^{0}_{S}  π^{+} + π^{}  0.692 
Λ  p + π^{}  0.639 
antiΛ  antip + π^{+}  0.639 
Candidate V0s are formed by combining together all possible pairs of opposite chargesign 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 πminus). 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.
Figure 1: Invariant mass spectrum under K0s hypothesis 
Figure 2: Invariant mass spectrum under Λ hypothesis 
Figure 3: Invariant mass spectrum under antiΛ 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; energyloss particle identification and geometrical cuts on the V0 candidates.
Ilya Selyuzhenkov January 30, 2008
jet trees by Murad Sarsour for pp2006 run, runId=7136022 (~60K events, no triggerId cuts yet)
R_EM1 >0.9 and R_EM2 < 0.9
cos(phi1  phi2) < 0.9
nChargeTracks1 < 2
0 < nEEMCtowers1 < 3
Ilya Selyuzhenkov February 13, 2008
Gammajet selection cuts are discussed here. There are 278 candidates found for runId=7136022.
Transverse momentum distribution for the gammajet candidates can be found here.
Figure 1: Vertex z distribution.
Red line presents gammajet candidates (scaled by x50). Black is for all dijet events.
Same data on a log scale is here.
EEMC response event by event for all 278 gammajet 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
(uplane is on the left, vplane is on the right).
In the left plot (uplane 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.
If m_{gamma gamma} value is negative, then the reconstruction procedure has failed
(for example, no uvstrips intersection found, or tower energy and uvstrips 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 vplane plot).
Second and third rows on each page show the energy deposition in the
tower, preshower1, preshower2, and postshower as a function of eta:phi (etaBin:phiBin).
Trying to isolate the real gammas which hits the calorimeter,
I have sorted events into different subsets based on the following set of cuts:
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 uvplane)
Figure 4: Sample gammajet candidate EEMC response
(all gammajet candidates selected according to these conditions can be found in this pdf file):
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 (dijet) candidates
selected according to these conditions can be found in this pdf file.
Ilya Selyuzhenkov February 20, 2008
After processing all available jettrees for pp2006 (ppProductionLong),
and applying all "gammajet" cuts (which are described below):
there are 47K dijet events selected
for pt1>7GeV there are 5,4K gammajet 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
jet trees by Murad Sarsour for pp2006 run, number of runs processed: 323
4.7M dijet events found (no triggerId cuts yet)
R_EM1 >0.9 and R_EM2 < 0.9
cos(phi1  phi2) < 0.9
nChargeTracks1 < 2
0 < nEEMCtowers1 < 3
Ilya Selyuzhenkov February 27, 2008
Gammajet isolation cuts:
R_EM1 >0.9 and R_EM2 < 0.9
cos(phi1  phi2) < 0.8
nCharge1 = 0
for each gammajet 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.
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.
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).
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)
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
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 vplane and sector 7 for uplane are noise.
Sector 9 has a hot strip (id ~ 120)
In sector 3, strips id=05 in vplane are hot (see figure 2 right, bottom)
Sectors 2 and 8 in uplane and sectors 3 and 9 in vplane have missing strips id=283288?
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
Ilya Selyuzhenkov March 12, 2008
Note: Dijet transverse momentum distribution for these candidates can be found on figure 11 at this page
Figure 1:Invariant mass distribution for gammajet candidates assuming pi0 (2gammas) hypothesys
Figure 2:Invariant mass distribution for gammajet candidates assuming pi0 (2gammas) 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
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.
Ilya Selyuzhenkov March 20, 2008
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 gammajet candidates requesting
no energy deposited in the EEMC preshower 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 gammajet candidates requesting requesting
no energy deposited in preshower 1 cluster and
no energy deposited in postshower cluster (this cut is not really essential in demonstrating the main idea)
Figure 3: Fit integral vs maximum residual for gammajet candidates requesting requesting
nonzero energy deposited in both clusters of preshower 1 and 2
Event by event EEMC response for gammajet candidates for the case of
no energy deposited in the EEMC preshower 1 and 2 can be found in this pdf file
Figure 4: Fit integral vs maximum residual for gammajet candidates requesting
no energy deposited in the EEMC preshower 1 and 2
Figure 5: Fit integral vs maximum residual for gammajet candidates requesting requesting
no energy deposited in preshower 1 cluster and
no energy deposited in postshower cluster
Figure 6: Fit integral vs maximum residual for gammajet candidates requesting requesting
nonzero energy deposited in both clusters of preshower 1 and 2
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 gammajet candidates using Monte Carlo shape requesting
no energy deposited in both clusters of preshower 1 and 2
Figure 8: Chi2/ndf for gammajet candidates using Monte Carlo shape requesting
nonzero energy deposited in both clusters of preshower 1 and 2
Less clear where to cut on chi2?
Figure 9: Chi2/ndf for gammajet candidates using Monte Carlo shape requesting
no energy deposited in both clusters of preshower 1 and 2
Figure 10: Chi2/ndf for gammajet candidates using Monte Carlo shape requesting
nonzero energy deposited in both clusters of preshower 1 and 2
Ilya Selyuzhenkov March 26, 2008
Definitions:
All results are for combined distributions from u and v planes: ([u]+[v])/2
Gammajet isolation cuts described here
Additional quality cuts:
Figure 1: F_peak vs maximum residual
for various cuts on energy deposited in the EEMC preshower 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^maxD_tail^max
Figure 4: Gamma transverse momentum vs jet transverse momentum
Figure 5: F_peak vs maximum residual
for various cuts on energy deposited in the EEMC preshower 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^maxD_tail^max
Figure 8: Gamma transverse momentum vs jet transverse momentum
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 vplane vs maximum strip in uplane
Figure 12:Chi2/ndf for gammajet candidates using Monte Carlo shape (combined for [u+v]/2 plane )
Figure 13:Chi2/ndf for gammajet candidates (combined for [u+v]/2 plane ) using Will's shape
Ilya Selyuzhenkov March 28, 2008
One interpretation of this can be that in Monte Carlo simulations
the contribution from the material in front of the detector is underestimated
Triple Gaussian fit gives a better description of the data shapes,
compared to the double Gaussian function (compare red and black lines on Figure 14)
Figure 1: EEMC SMD shape comparison for various preshower cuts
(black points shows uplane shape only, vplane results can be found here)
Figure 2: EEMC SMD shape comparison for various preshower cuts with gammajet pt cut of 7GeV
(black points shows uplane shape only, vplane results can be found here)
Figure 3: Shapes with an additional cut on 2gamma candidates within pi0 invariant mass range.
Sample invariant mass distribution using "simple" pi0 finder can be found here
(black points shows uplane shape only, vplane results can be found here)
Figure 4: Shapes for the candidates when "simple" pi0 finder failed to find a second peak
(black points shows uplane shape only, vplane 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 uplane only, vplane results can be found here)
Results for side residual (together with pt, eta, phi distributions) for gammajet candidates can be found at this web page
Red histograms on Figures 68 shows chi2 distribution from MCphotons (normalized at chi2=1.4)
Blue histograms on Figures 68 shows chi2 distribution from MCpions (normalized at chi2=1.4)
Figure 6: Chi2/ndf for gammajet candidates using Monte Carlo shape
Figure 7: Chi2/ndf for gammajet candidates using Will's shape (derived from eta candidates based on Weihong's pi0finder)
Figure 8: Chi2/ndf for gammajet candidates using Pibero's shape (derived from eta candidates)
Ilya Selyuzhenkov April 02, 2008
Figure 1: EEMC SMD shape comparison for various preshower cuts
Note:Only MC gammajet shape (open red squares) is the same on all plots
Ilya Selyuzhenkov April 02, 2008
Figure 1: Side residual for various cuts on energy deposited in the EEMC preshower 1 and 2
No EEMC SMD based cuts
Figure 2: Side residual for various cuts on energy deposited in the EEMC preshower 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 preshower 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.13 on vertical axis)
Figure 5: Signal (green: m < 0) vs background (black, red) separation
Ilya Selyuzhenkov April 02, 2008
Figure 1: Side residual for various cuts on energy deposited in the EEMC preshower 1 and 2
No EEMC SMD based cuts
Ilya Selyuzhenkov April 03, 2008
Hi Ilya,
I think you gave up on the chisquared 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 chisquared 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 chisquared/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 chisquared 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.
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 gammajet candidates) for a different pre1, pre2 conditions
2. Upper right plot shows binbybin 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
Ilya Selyuzhenkov April 09, 2008
Data sample:
MonteCarlo gammajet sample for partonic pt range of 57, 79, 911,1115, 1525, 2535 GeV.
Analysis: Simulated MuDst files were first processed through jet finder algorithm (thanks to Renee Fatemi),
and later analyzed by applying gammajet 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.
Figure 1:Comparison between shower shape profile for data and MC.
Black circles shows results for MC gammajet sample (all partonic pt).
For vplane results see this figure
Figure 2:Gamma vs jet transverse momentum.
Figure 3:Gamma vs jet azimuthal angle.
Figure 4:Gamma vs jet pseudorapidity.
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 preshower 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 gammajet 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^maxD_tail^min: click here
Figure 8:Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u and vplanes
Figure 8a:
Same as figure 8, but for gammajet 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 vplanes
Figure 8b:
Comparison between gammajet 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 vplanes
Figure 8c:
Comparison between gammajet candidates from MonteCarlo 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 vplanes
Figure 9: Jet neutral energy fraction
Figure 10: High vstrip vs ustrip
Figure 11: energy post shower (3x3 cluster)
Figure 12: Peak energy SMDu
Figure 13: Peak energy SMDv
Figure 14: Gamma phi
Figure 15: Gamma pt
Figure 16: Gamma eta
Figure 17: Delta gammajet pt
Figure 18: Delta gammajet eta
Figure 19: Delta gammajet phi
Figure 20:chi2 distribution using "standard" MC shape
Ilya Selyuzhenkov April 16, 2008
Figure 1: Sided residual for raw MC (partonic pt 911)
Figure 2: Sided residual for datadriven MC (partonic pt 911)
Figure 3: Sided residual for data (pp Longitudinal 2006)
Figure 4: Number of events which passed various cuts (MC data, partonic pt 911)
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 535)
(same plot for partonic pt 911)
Figure 2: Sided residual for datadriven MC (partonic pt 535)
(same plot for partonic pt 911)
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 uvintersection wrt vertex
(same on a linear scale)
Figure 7: pt(gamma) from geant record vs
pt(jet) as found by the jetfinder
Figure 8: gamma pt distribution:
datadriven MC (red) vs gammajet candidates from pp2006 longitudinal run (black).
MC distribution normalized to data at maximum for each preshower condition
Figure 9: Number of events which passed various cuts (MC data, partonic pt 535)
Red: cuts applied independent
Black: cuts applied sequential from left to right
Ilya Selyuzhenkov April 23, 2008
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 datadriven gammajet MC events (partonic pt 535)
Figure 2:Sided residual for datadriven jetjet MC events (partonic pt 355)
Figure 3:Sided residual for data (pp Longitudinal 2006)
Figure 4:pt(gamma) vs pt(jet) for datadriven gammajet MC events (partonic pt 535)
Figure 5:pt(gamma) vs pt(jet) for datadriven jetjet MC events (partonic pt 355)
Figure 6:pt(gamma) vs pt(jet) for data (pp Longitudinal 2006)
Ilya Selyuzhenkov May 05, 2008
Data samples:
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)
You can find sided residual 2D 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
Ilya Selyuzhenkov May 08, 2008
Figure 1:y:x EEMC position for gammajet candidates:
Pythia gammajet sample (~170K events). Partonic pt range 535 GeV.
Figure 2:y:x EEMC position for gammajet candidates:
Pythia QCD bg sample (~4M events). Partonic pt range 365 GeV.
Figure 3:y:x EEMC position for gammajet candidates:
pp2006 (long) data [eemchttpmbl2gamma:137641 trigger]
Figure 3b:y:x EEMC position for gammajet candidates:
pp2006 (long) data [eemchttpmbl2gamma:137641 trigger]
pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.
Figure 4:High vstrip vs high ustrip.
Pythia gammajet sample (~170K events). Partonic pt range 535 GeV.
Figure 5:High vstrip vs high ustrip:
Pythia QCD bg sample (~4M events). Partonic pt range 365 GeV.
Figure 6:High vstrip vs high ustrip:
pp2006 (long) data [eemchttpmbl2gamma:137641 trigger]
Figure 6b:High vstrip vs high ustrip:
pp2006 (long) data [eemchttpmbl2gamma:137641 trigger]
pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.
Ilya Selyuzhenkov May 09, 2008
For a three data samples (pp2006 [long], MC gammajet, 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).
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)
Ilya Selyuzhenkov May 14, 2008
Correlation between gammacandidate 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. 13).
Gamma pt distribution, yield and signal to background ratio plots
for a cut of R_cluster >0.99 are shown below in Figs. 13.
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)
Figure 4: pt asymmetry between gamma and the away side jet (R_cluster >0.9)
for a three data samples (pp2006[long] data, gammajet 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, gammajet 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, gammajet 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.
Ilya Selyuzhenkov May 15, 2008
Figure 1:Vertex z distribution for pp2006 (long) data [eemchttpmbl2gamma: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.
Ilya Selyuzhenkov May 20, 2008
Selecting only dijet events identified by the STAR jet finder algorithm,
with jets pointing opposite in azimuth:
cos(phi_jet1  phi_jet2) < 0.8
Data sample:
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 zvertex bins
Ilya Selyuzhenkov May 21, 2008
Data sample:
Subset of 441 etameson candidates from Will's analysis.
additional QA info (detector eta, pre1, pre2, etc)
has been added to pi0tree 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:
etameson purity within the invariant mass region [0.5, 0.65] is about 72%
Most of the etacandidates has detector pseudorapidity less or about 1.4,
what may limits applicability of datadriven shower shapes
derived from these candidates for higher pseudorapidity region,
where we have most of the background for the gammajet
analysis due to lack of TPC tracking
zvertex distribution is very asymmetric, and peaked around 50cm.
Only a few candidates has a positive zvertex values.
Figure 1: Etameson 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 etameson invariant mass vs
azimuthal angle (upper left), pseudorapidity (upper right),
zvertex (bottom right), and detector pseudorapidity (bottom right)
Ilya Selyuzhenkov May 27, 2008
Figure 1: Shower shapes and triple Gaussian fits for photons from etameson
sorted by different conditions of EEMC 1st and 2nd preshower 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((x0.46016)/0.574864))+0.272997*exp(0.5*sq((x0.46016)/1.84608))+0.0585682*exp(0.5*sq((x0.46016)/5.49802))
Pre1=0 Pre2>0
0.0694729*exp(0.5*sq((x0.493468)/5.65413))+0.615724*exp(0.5*sq((x0.493468)/0.590723))+0.314777*exp(0.5*sq((x0.493468)/2.00192))
Pre1>0 Pre2>0
0.0955638*exp(0.5*sq((x0.481197)/5.59675))+0.558661*exp(0.5*sq((x0.481197)/0.567596))+0.345896*exp(0.5*sq((x0.481197)/1.9914))
Shower shapes comparison between different data sets:
Some observations:
Shapes for gammas from etameson decay
are in a good agreement with those from MC gammajet sample
(compare red squares with blue triangle in Fig. 2 and 3).
MC gammajet shapes obtained by running a full gammajet reconstruction algorithm,
and this agreement indicates that we are able to reconstruct gamma shapes
which we put in with datadriven shower shape library.
MC gammajet 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 datadriven MonteCarlo.
There is no match between MonteCarlo QCD background jets and pp2006 data
for the case when both preshower 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 gammajet candidates.
This shows that we still do not reproduce SMD response for our background events
in our datadriven MonteCarlo simulations
(note, that in MonteCarlo 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 gammajet candidates obtained with the same gammajet reconstruction algorithm
for three different data samples (pp2006, gammajet 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.
Ilya Selyuzhenkov May 30, 2008
Three data sets:
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: Gammajet delta pt distribution.
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.
Figure 7: Gammajet delta eta distribution.
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.
Figure 8: Gammajet delta phi distribution.
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.
Ilya Selyuzhenkov June 04, 2008
Three data sets:
Figure 1: Gamma candidate EEMC preshower 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 preshower 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 postshower 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 uplane energy [5strip cluster] (Figure for vplane)
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.
Figure 6: Total minus gamma candidate (3x3 cluster) energy in EEMC preshower 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 preshower 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 postshower 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 (5strip cluster) energy in EEMC SMD[uv] layer
pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied.
Ilya Selyuzhenkov June 09, 2008
Three data sets:
Numerical values for different ptbins from Fig. 12
Figure 1: Gamma pt distribution for R_cluster >0.9.
No energy in both preshower layer (left plot), and
No energy in preshower1 and nonzero energy in preshower2 (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 preshower1 layer (left plot), and
nonzero energy in preshower1 (right plot)
For more details (yield, ratios, all pre12 four conditions, etc) see figures 13 here.
Figure 3: Gamma pt distribution for R_cluster >0.99.
For more details (yield, ratios, all pre12 four conditions, etc) see figures 13 here.
Ilya Selyuzhenkov June 10, 2008
Note: No background subtraction has been done yet
The case of preshower1=0 (left plots) roughly has 1:1 signal to background ratio,
while preshower1>0 (right plots) have 1:10 ratio (See MC to data comparison for details).
Data sets:
Figure 1: Gammajet 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: Gammajet candidate A_LL vs x_gluon.
Figures for related epsilon_LL and 1/Lum scaled by a factor 10^7
Figure 3: Gammajet candidate A_LL vs x_quark.
Figures for related epsilon_LL and 1/Lum scaled by a factor 10^7
Figure 4: Gammajet candidate A_LL vs away side jet pt.
Figures for related epsilon_LL and 1/Lum scaled by a factor 10^7
Ilya Selyuzhenkov June 18, 2008
Photonjet reconstruction with the EEMC detector  Part 1: pdf or odp
Data samples (pp2006, MC gJet, MC QCD bg)
and gammajet reconstruction algorithm:
Comparing pp2006 with MonteCarlo simulations scaled to the same luminosity
(EEMC preshower sorting):
EEMC SMD shower shapes from different data samples
(pp2006 and datadriven MonteCarlo):
Sided residual plots: pp2006 vs datadriven MonteCarlo
(gammas from eta meson: 3 gaussian fits)
Various cuts study:
Some QA plots:
A_LL reconstruction technique:
Work in progress... To do list:
Ilya Selyuzhenkov July 07, 2008
Data sets:
Figure 1: Correlation between 3x3 cluster energy in preshower2 vs. preshower1 layers
Figure 1a: Distribution of the 3x3 cluster energy in preshower1 layer (zoom in for Epre1<0.03 region)
(pp2006 data vs. MC gammajet and QCD events)
Figure 2: Shower shapes after preshower1 < 5MeV cut.
Shapes are narrower than those without pre1 cut (see Fig. 2)
Figure 3: Gamma pt distribution with preshower1 < 5MeV cut.
Compare with distribution withoud preshower1 (Fig. 3)
Figure 4: Fitted peak vs. maximum sided residual (no preshower1 cuts)
Only points for pp2006 data are shown.
Figure 5: Fitted peak vs. maximum sided residual (after preshower1 < 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 preshower1 cuts).
Figure 6: Distribution of maximum sided residual with preshower1 < 5MeV cut.
Ilya Selyuzhenkov July 16, 2008
Three data sets:
My simple gammagamma 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: Gammagamma 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
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.
Figure 5: Shower shapes (no pre1 and no invMass cuts).
Good match between shapes in case of no energy in preshower1 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 etameson shapes (blue) and data (black).
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]
Ilya Selyuzhenkov July 22, 2008
Figure 1: Shower shapes: no energy cuts, only 12 strips from peak (left uplane, right vplane).
Figure 1a: Shower shapes: no energy cuts, 150 strips from peak (left uplane, right vplane).
Figure 2: Shower shapes Energy>8GeV (left uplane, right vplane).
Figure 3: Shower shapes Energy<=8GeV (left uplane, right vplane).
Figure 8: Number of library candidates per sector.
Figure 9: Transverse momentum vs. energy.
Figure 10: Distance from center of the detector vs. energy.
Ilya Selyuzhenkov July 29, 2008
Data sets:
Latest datadriven shower shape replacement library:
Figure 1: Shower shapes for uplane [12 strips]
Shower shapes for the library are for the E>8GeV bin.
Figure 2: Shower shapes for vplane [12 strips]
Figure 3: Shower shapes for uplane [expanded to 40 strips]
Figure 4: Shower shapes for vplane [expanded to 40 strips]
Ilya Selyuzhenkov August 14, 2008
Data sets:
Datadriven maker with bug fixed multishape replacement:
Figure 1: Shower shapes for uplane [12 strips]
Shower shapes for the library are for the E>8GeV bin.
Open squares and triangles represents raw [without ddmaker]
MC gammajet and QCD background shower shapes respectively
Figure 2: Shower shapes for vplane [12 strips]
Figure 3: Shower shapes for uplane [expanded to 40 strips]
Dashed red and green lines represents raw [without ddmaker]
MC gammajet and QCD background shower shapes respectively
Figure 4: Shower shapes for vplane [expanded to 40 strips]
Ilya Selyuzhenkov August 19, 2008
Data sets:
Note: Due to lack of statistics for 2008 data, no pt cuts applied on gammajet candidates for both 2006 and 2008 date.
Figure 1: Shower shapes for uplane [pp2006 data: eemchttpmbl2gamma trigger]
Figure 2: Shower shapes for vplane [pp2006 data: eemchttpmbl2gamma trigger]
Figure 3: Shower shapes for uplane [pp2008 data: fmsslow trigger]
Figure 4: Shower shapes for vplane [pp2008 data: fmsslow trigger]
Ilya Selyuzhenkov August 25, 2008
Data sets:
Event selection:
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. zvertex distribution for the jet1 (jet with largest NEF) .
Figure 4: eta vs. zvertex distribution for the second jet.
Figure 5: Transverse momentum distribution for jet1.
Ilya Selyuzhenkov August 26, 2008
Data sets:
Datadriven library:
Figure 1: Preshower1 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 vplane [Pre1<10MeV cut]
Figure 3: Shower shapes for uplane [Pre1<10MeV cut]
Definitions for side residual plot (F_peak, F_tal, D_tail) can be found here
For a moment same 3gaussian shape is used to fit SMD response for all preshower bins.
Algo needs to be updated with a new shapes sorted by preshower 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]
Ilya Selyuzhenkov August 27, 2008
Data sets:
Figure 1: High u vs. v strip id distribution for different preshower conditions.
Left column: QCD jets, middle column: gammajet, right columnt: pp2006 data
Figure 2: x vs. y position of the gammacandidate within EEMC detector
for different preshower conditions.
Left column: QCD jets, middle column: gammajet, right columnt: pp2006 data
Figure 3:Reconstructed vs. generated (from geant record) gamma pt for the MC gammajet sample.
Preshower1<10MeV cut applied.
Figure 4:Reconstructed vs. generated (from geant record) gamma eta for the MC gammajet sample.
Preshower1<10MeV cut applied.
Figure 5:Reconstructed vs. generated (from geant record) gamma phi for the MC gammajet sample.
Preshower1<10MeV cut applied.
Ilya Selyuzhenkov September 02, 2008
Data sets:
Shower shape fitting procedure:
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 ))"
Figure 1: MC gammajet shower shapes and fits for uplane
Results from single, double and triple Gaussian fits (using from 9 to 15 strips) are shown.
Figure 2: Same as figure 1. but from vplane
Figure 3: MC gammajet results using triple Gaussian fits within 12 strips from a peak.
Left: uplane. Right: vplane
Figure 4: Combined fit results from MC gammajet 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:
Figure 6: Same as Fig. 3, but for gammajet candidates from pp2006 data
Figure 7: Same as Fig. 5, but for gammajet candidates from pp2006 data
Ilya Selyuzhenkov September 09, 2008
Data sets:
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 preshower conditions and u and vplanes 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_tailF_tail). This is our maximum sided residual.
Plot F_peak vs. max(D_tailF_tail). This is sided residual plot.
(implementation for this item is in progress)
Based on MC gammajet 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 preshower condition.
Columns [data sets]: 1. MC QCD background; 2. gammajet; 3. pp2006 data
Rows [preshower 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_tailF_tail) distribution (projection on horizontal axis from Fig.1)
Some observations:
Results for pp2006 and MC gammajet 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)
Ilya Selyuzhenkov September 16, 2008
These results are obsolete.
Please use this link instead
Data sets:
Notations used in the plots:
Figure 1: D_peak from [U+V]/2.
Figure 2: U/V asymmetry for D_peak: [UV]/[U+V]
Figure 3: F_peak from [U+V]/2.
Figure 4: U/V asymmetry for F_peak: [UV]/[U+V]
Figure 5: (D_peak  F_peak)/D_peak asymmetry
Ilya Selyuzhenkov September 23, 2008
Data sets:
Notations used in the plots:
Figure 1: D_peak from [U+V]/2.
Figure 2: (D_peak  F_peak)/D_peak asymmetry
Ilya Selyuzhenkov September 23, 2008
Figure 1: D_peak vs. [rightleft] D_tail
Ilya Selyuzhenkov September 23, 2008
Data sets:
Notations used in the plots:
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 preshower fired case) reveals some difference
between MC gammajet 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_tailF_tail) (projection on horisontal axis for Fig. 1)
One can get an idea of signal/background separation (red vs. black) depending on preshower condition.
Figure 4: Mean < max(D_tailF_tail) > vs. D_peak (profile on vertical axis from Fig. 1)
For gammajet sample average sided residual is independent on D_peak energy
and has a slight positive shift for all preshower>0 conditions.
For large D_peak values (D_peak>0.16) MC gammajet 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 gammajet 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_tailF_tail) (profile on horisontal axis from Fig. 1)
For "nopreshower fired" case MC gammajet sample has a large average values than that from pp2006 data.
This reflects the same difference between pp2006 and MC gammajet 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 7: D_peak vs. gamma 3x3 tower cluster energy
Figure 8: 3x3 cluster tower energy distribution
Figure 9: Gamma pt distribution
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_tailF_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 gammajet sample (middle column)
are on the left side from vertical axis.
Figure 11: "Rotated" max(D_tailF_tail) [projection on horizontal axis for Fig. 10]
Cut on "Rotated" max(D_tailF_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]
Ideally, instead of straight line one needs to use
an actual shape of side residual distribution for MC gammajet sample.
This shape can be extracted and parametrized by the following procedure:
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_tailF_tail) for different slices in D_peak (scaled by the integral for each slice)
Ilya Selyuzhenkov September 30, 2008
Data sets:
Notations used in the plots:
Figure 1: Sided residual plot: D_peak vs. max(D_tailF_tail)
Red lines show 4th order polynomial functions, a*x^4,
which have 80% of MC gammajet counts on the left side.
These lines are obtained independently for each of preshower condition
based on fit procedure shown in Fig. 3 below.
Figure 2: max(D_tailF_tail) distribution
(projection on horizontal axis from sided residual plot, see Fig. 1 above)
Figure 3: max(D_tailF_tail) [at 80%] vs. D_peak.
For each slice (bin) in D_peak variable, the max(D_tailF_tail) value
which has 80% of gammajet candidates on the left side are plotted.
Lines represent fits to MC gammajet 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_tailF_tail) values.
(compare with Fig. 1: Now 80% of MC gammajet counts are on the left side from vertical axis)
Figure 5: Horizontal distance from 4th order polynomial function to max(D_tailF_tail)
[Projection on horizontal axis from Fig. 4]
Based on this plot one can obtain purity, efficiency, and rejection plots (see Fig. 6 below)
Horizontal distance plotted in Fig. 5 can be used as a cut
separating gammajet signal and QCDjets background,
and for each value of this distance one can define
gammajet purity, efficiency, and QCDbackground rejection:
gammajet purity is defined as the ratio of
the integral on the left for MC gammajet data sample, N[gjet]_left,
to the sum of the integrals on the left for MC gammajet and QCD jets, N[QCD]_left, data samples:
Purity[gammajet] = N[gjet]_left/(N[gjet]_left+N[QCD]_left)
gammajet efficiency is defined as the ratio of
the integral on the left side for MC gammajet data sample, N[gjet]_left,
to the total integral for MC gammajet data sample, N[gjet]:
Efficiency[gammajet] = N[gjet]_left/N[gjet]
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[gjet] vs. efficiency[gjet] (upper left);
rejection[QCD] vs. efficiency[gjet] (upper right);
purity[gjet] vs. rejection[QCD] (lower left);
pp2006 to MC ratio, N[pp2006]/(N[gjet]+N[QCD]), vs. horizontal distance from Fig. 5 (lower right)
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):
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:
Ilya Selyuzhenkov October 14, 2008
Data sets:
Figure 1: Horizontal distance from 4th order polynomial function to max(D_tailF_tail)
See this page for definition and more details
Figure 2:
purity[gjet] vs. efficiency[gjet] (upper left);
rejection[QCD] vs. efficiency[gjet] (upper right);
purity[gjet] vs. rejection[QCD] (lower left);
pp2006 to MC ratio, N[pp2006]/(N[gjet]+N[QCD]), vs. horizontal distance (lower right)
Ilya Selyuzhenkov October 15, 2008
Data sets:
Some observations:
Figure 1a: partonic pt for gammajet [old] events
after analysis cuts and partonic pt bin weighting
(Note:Arbitrary absolute scale)
Figure 1b: partonic pt for gammajet [gammafiltered] 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 [gammafiltered] 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: gammafiltered MC vs. pp2006 data (scaled to the same luminosity)
Ilya Selyuzhenkov October 15, 2008
Data sets:
Figure 1: Horizontal distance from sided residual plot: R_cluster > 0.9
(see Figs. 15 from this post for horizontal distance definition)
Figure 2: Purity/efficiency/rejection, and data to MC[gammajet+QCD] ratio plots: R_cluster > 0.9
(see text above Fig. 6 from this post for purity, efficiency, and background rejection definition)
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[gammajet+QCD] ratio plots: R_cluster > 0.98
Ilya Selyuzhenkov October 21, 2008
Data sets:
Some comments:
Fig. 13, upper left plots (pre1=0 pre2=0) show that
average energy per strip in datadriven gammajet 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 datadriven gammajet 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 gammajet simulations (red line) match pp2006 data (black line)
in the region where signal to background ratio is high, E_smd(25strips)>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 datadriven gammajet simulations and pp2006 data.
This probably reflects the way we scale our library shower shapes
in datadriven 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 etameson 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 5strip energy for raw MC and pp2006 in Fig. 6
corresponds to "known" difference in shower shapes from raw MonteCarlo and real data.
Figure 1: SMD shower shapes: data, raw, and datadriven MC (40 strips).
Vertical axis shows average energy per strip (no overall shower shapes scaling)
Figure 2: Shower shapes: data, raw, and datadriven MC (12 strips)
Figure 3: Shower shapes: data, raw, and datadriven MC (5 strips)
Figure 4: 25 strips SMD cluster energy for raw MonteCarlo
(Note: type in xaxis lables, should be "25 strip peak" instead of 5)
Figure 5: 25 strips SMD cluster energy for datadriven MonteCarlo
Figure 6: 5 strips SMD peak energy for raw MonteCarlo
Figure 7: 5 strips SMD peak energy for datadriven MonteCarlo
Figure 8:Energy from the right tail (up to 30 strips) for raw MonteCarlo
Figure 9:Energy from the right tail (up to 30 strips) for datadriven MonteCarlo
Ilya Selyuzhenkov October 27, 2008
Data sets:
Shower shapes scaling options in datadriven maker:
In all figures below (exept for pp2006 data and raw MonteCarlo)
the SMD based shower shape scaling has been used.
Figure 1: SMD shower shapes: data, raw, and datadriven MC (40 strips).
Vertical axis shows average energy per strip (no overall shower shapes scaling)
Figure 2: Shower shapes: data, raw, and datadriven MC (12 strips)
Figure 3: Shower shapes: data, raw, and datadriven MC (5 strips)
Figure 4: 25 strips SMD cluster energy for datadriven MonteCarlo
(SMD based shower shape scaling)
Figure 5: 25 strips SMD cluster energy for raw MonteCarlo
Note, the difference between results in Fig. 4 and 5. for MC gammajets (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 5strips cluster > 3" cut.
In datadriven MonteCarlo we may have shower shapes
with small number of strips fired (rejected in raw MonteCarlo)
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)
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:
Figure 1: Case A
Figure 2:Case B
Figure 3:Case C
Figure 4:Case D
Figure 1: Case A
Figure 2:Case B
Figure 3:Case C
Figure 4:Case D
Figure 1: Case A
Figure 2: Case B
Figure 3: Case C
Figure 4: Case D
Figure 1: Case A
Figure 2:Case B
Figure 3:Case C
Figure 4:Case D
Figure 1: Case A
Figure 2:Case B
Figure 3:Case C
Figure 4:Case D
Figure 1: Case A
Figure 2:Case B
Figure 3:Case C
Figure 4:Case D
Figure 1: Case A
Figure 2:Case B
Figure 3:Case C
Figure 4:Case D
Figure 1: Case A
Figure 2:Case B
Figure 3:Case C
Figure 4:Case D
Ilya Selyuzhenkov November 06, 2008
Ilya Selyuzhenkov November 11, 2008
Data sets:
Figure 1: Reconstructed gamma pt for dijet 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 gammajet 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 dijet events and the same cuts as in Fig. 1
List of cuts (sorted by bin number in Figs. 2 and 3):
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 dijets 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 gammajet and QCDjets events (cuts applied independently)
lower right:ratio of pp2006 data vs. MC sum of gammajet 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
Ilya Selyuzhenkov November 18, 2008
all gammajet candidate selection cuts except 3x3/r=0.7 energy isolation cut
Figure 4b: 3x3/0.7 ratio but only using towers which passed jet finder threshold
Ilya Selyuzhenkov November 21, 2008
all gammajet candidate selection cuts except 3x3/r=0.7 energy isolation cut
There are two sets of figures in links below:
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)
Figure 1a: 2x1/3x3 energy fraction [number of counts per given fraction]
Figure 2a: 2x2/3x3 energy fraction [number of counts per given fraction]
Ilya Selyuzhenkov November 25, 2008
Fig.2 [lower right, 5th bin] shows that
charge particle veto also independent from other cuts
List of cuts (sorted according to bin number in Figs. 13. [No SMD sided residual cuts]):
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 dijets 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
Ilya Selyuzhenkov December 08, 2008
Figure 1: EEMC x vs. y position of photon candidate for 2008 data sample
Problem with preshower 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 preshower energy for sector 10 (black points) for days 61, 62, 64, and 67.
All dijet events for pp2008 data are shown (no gammajet 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
No problem with pp2008 data have been found,
except that for some runs (mostly on days 61, 62, 64, and 67)
EEMC preshower layer for sector 10 was off.
Figure 5: Vertex z distribution:
All gammajet 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):
Ilya Selyuzhenkov December 09, 2008
Ilya Selyuzhenkov December 09, 2008
Figure 1: Vertex z distribution:
All gammajet 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):
Ilya Selyuzhenkov December 09, 2008
Figure 1: Vertex z distribution:
All gammajet 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):
Ilya Selyuzhenkov December 09, 2008
Figure 1: Vertex z distribution:
All gammajet 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):
Ilya Selyuzhenkov December 09, 2008
Figure 1: Vertex z distribution:
All gammajet 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):
Ilya Selyuzhenkov December 09, 2008
Figure 1: Vertex z distribution:
All gammajet 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):
Ilya Selyuzhenkov December 11, 2008
Presentation in pdf or open office file format
Ilya Selyuzhenkov December 16, 2008
Gammajet isolation cuts except 3x3/r=0.7 energy isolation cut
Figure 1: (reproducing old results with ddmaker fix)
transverse momentum and vertex z distributions
before (with ideal gains/pedestals) and
after (with realistic gains/pedestal tables) ddmaker fix are in a good agreement.
For details on "ddmaker 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.
Figure 2: Vertex z distribution with and without trigger condition in simulations
(emulated trigger: eemchttpmbL2gamma [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 preshower1>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) MonteCarlo:
no (upper) with (lower) L2egamma trigger condition
No photon pt and no jet pt cuts
Figure 6: gamma candidate pt for QCD MonteCarlo: no L2egamma trigger condition
No photon pt and no jet pt cuts
Figure 7: gamma candidate pt for QCD MonteCarlo: L2egamma trigger condition applied (id:137641)
No photon pt and no jet pt cuts
Ilya Selyuzhenkov December 19, 2008
Gammajet isolation cuts except 3x3/r=0.7 energy isolation cut
Figure 1: Parton pt distibution for gammajet candidates from Pythia QCD sample
with various pt and l2gamma trigger conditions
Figure 2: Parton pt distibution for gammajet candidates from Pythia prompt photon sample
with various pt and l2gamma trigger conditions
Ilya Selyuzhenkov January 08, 2009
(concentrated on preshower1>0 case
which has better statistics for QCD MonteCarlo):
Figure 1: Vertex z distribution
Figure 4: gammajet pt asymmetry: (pt_gamma  pt_jet)/pt_gamma
Figure 5: gamma pt vs. away side jet pt
1st column: triggered pp2006 data
2nd column: gammajet MC (l2gamma trigger on)
3rd column: QCD background MC (l2gamma trigger on)
Ilya Selyuzhenkov January 20, 2009
Note:
this is an update with 10x more statitstics for QCD 915GeV parton pt bin.
See this post for old results.
Figure 1: Vertex z distribution
Figure 4: gammajet pt asymmetry: (pt_gamma  pt_jet)/pt_gamma
Figure 5: gamma pt vs. away side jet pt
1st column: triggered pp2006 data
2nd column: gammajet MC (l2gamma trigger on)
3rd column: QCD background MC (l2gamma trigger on)
Figure 6: pp2006 data to Monte Carlo sum [QCD + gammajet] ratio
for preshower1>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
Ilya Selyuzhenkov January 27, 2009
All figures:
Ilya Selyuzhenkov January 27, 2009
All figures:
Figure 1: Vertex z distribution
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
Ilya Selyuzhenkov February 02, 2009
All figures:
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)
Ilya Selyuzhenkov February 03, 2009
Each figure has:
Figure 1: Vertex z distribution
Figure 3: Corrected for vetrex photon eta
Figure 4: Away side jet detector eta
Ilya Selyuzhenkov February 06, 2009
Partonic pt range 225 GeV.
Each figure has:
Note: There is a "preshower sector 10 problem" for pp2008 data,
which results in migration of small fraction of events with preshower>0 into
preshower=0 bin (first zero bins in Fig.1 and 2. below).
For preshower>0 case this only affects overall normalization of pp2008 data,
but not the shape of preshower energy distributions.
I'm running jetfinder+my software to get more statistics from pp2008 data set,
and after more QA will produce list of runs with "preshower sector 10 problem",
so to exclude them in the next iteration of my plots.
Ilya Selyuzhenkov February 06, 2009
Partonic pt range 225 GeV.
Each figure has:
Figure 6: gammajet pt balance
Figure 7: Photon neutral energy fraction
Figure 8: Jet neutral energy fraction
Figure 9: cos(phi_gammaphi_jet)
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
Ilya Selyuzhenkov February 16, 2009
Partonic pt range 225 GeV.
Each figure has:
Slides: download pdf
Link for CIPANP abstract
Title:
"Photonjet 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
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 inputoutput map.
They have been shown to approximate the performance of optimal statistical classifiers in difficult problems.
TMultiLayerPerceptron class in ROOT
mlpHiggs.C example
Netwrok structure:
r3x3, (pt_gammapt_jet)/pt_gamma, nCharge, bBtow, eTow2x1: 10 hidden layers: one output later
Figure 1:
Figure 2: Input parameters vs. network output
Row: 1: MC QCD, 2: gammajet, 3 pp2006 data
Vertical axis: r3x3, (pt_gammapt_jet)/pt_gamma, nCharge, bBtow, eTow2x1
Horisontal axis: network output
ROOT implementation for LDA and MLP:
LDA configuration: default
MLP configuration:
Input parameters (same for both LDA and MLP):
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:
Figure 3: Data to MonteCarlo comparison for LDA (upper plots) and MLP (lower plots)
Good (within ~ 10%) match between data nad MonteCarlo
a) up to 0.8 for LDA discriminant, and b) up to 0.7 for MLP.
Figure 4: Data to MonteCarlo comparison for input parameters
from left to right
1) pt_gamma 2) pt_jet 3) r3x3 4) gammajet pt balance 5) N_ch[gamma] 6) N_eTow[gamma] 7) N_bTow[gamma]
Colour coding: black pp2006 data, red gammajet MC, green QCD MC, blue gammajet+QCD
Figure 5: Data to MonteCarlo comparison:
correlations between input variables (in the same order as in Fig. 4)
and LDA classifier discriminant (horizontal axis).
1st raw: QCD MC; 2nd: gammajet MC; 3rd: pp2006 data; 4th: QCD+gammajet MC
Endcap photonjet update at the STAR Collaboration meeting
April 2009 posts
The STAR spin program with longitudinally polarized proton beams
ROOT implementation for LDA:
LDA configuration: default
LDA input parameters:
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 (LDAdsicriminant>0, no preshower1 cut)
Figure 4: SMD energy in 25 central strips (LDAdsicriminant>0, preshower1 < 10MeV)
Figure 5: Maximum residual (LDAdsicriminant>0, no preshower1 cut)
Figure 6: Maximum residual (LDAdsicriminant>0, preshower1 < 10MeV)
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 LDAdsicriminant>0, preshower1 < 10MeV)
Figure 10:LDA (no SMD): Efficiency, rejection, purity plots
Figure 11: LDA with SMD: Efficiency, rejection, purity plots
ROOT implementation for LDA:
LDA configuration: default
LDA input parameters (includes SMD inromation of the distance from max sided residual plot):
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 preshower energy
Right plot for each preshower condition shows the ratio of pp2006 data to sum of the MonteCarlo samples
Colour coding:
black pp2006 data, red gammajet MC, green QCD MC, blue gammajet+QCD
(combined plot for all preshoer bins can be found here)
Figure 2: SMD energy in 5 central strips sorted by preshower energy
(combined plot can be found here)
Figure 3: Maximum residual sorted by preshower energy
(combined plot can be found here)
Figure 4: LDA discriminant. Note: LDA algo trained for each preshower condition independently
Figure 5: LDA: Efficiency, rejection, purity vs. discriminant, sorted by preshower energy
Figure 6: LDA: Efficiency, rejection, purity plots sorted by preshower energy
For each preshower condition each plot has 4 figures:
ROOT implementation for LDA:
LDA configuration: default
LDA input parameters Set0:
The number of strips in SMD u or v planes is required to be greater than 3
Preshower sorting (energy in tiles under 3x3 tower patch):
Photon pt and rapidity cuts:
Figure 0: photon pt distribution for preshower1<0.01
Colour coding:
black pp2006 data, red gammajet MC, green QCD MC, blue gammajet+QCD
Figure 1: LDA discriminant with Set0: Data to MonteCarlo comparison (pt>7GeV cut)
Right plot for each preshower condition shows the ratio of pp2006 data to sum of the MonteCarlo samples
Colour coding:
black pp2006 data, red gammajet MC, green QCD MC, blue gammajet+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
Figure 6: LDA discriminant with Set1: Data to MonteCarlo comparison
Figure 7: rejection vs. efficiency
Figure 8: purity vs. efficiency
Figure 9: purity vs. rejection (click link to see the figure)
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)
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)
ROOT implementation for LDA:
LDA configuration: default
LDA input parameters Set0:
The number of strips in SMD u or v planes is required to be greater than 3
Preshower sorting (energy in tiles under 3x3 tower patch):
Integrated yields per preshower 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 
photonjet  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:
Figure 1: LDA discriminant with Set0: Data to MonteCarlo comparison (pt>7GeV cut)
Right plot for each preshower condition shows the ratio of pp2006 data to sum of the MonteCarlo samples
Colour coding:
black pp2006 data, red gammajet MC, green QCD MC, blue gammajet+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
For this post LDA input parameters Set4 has been used
LDA for various preshower bins is trained independetly,
and later results with preshower1<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
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)
Figure 5: pt > 9GeV, efficiency@70
Figure 6: pt > 9GeV, purity@35
Figure 7: pt > 10GeV, efficiency@70
Figure 8: pt > 10GeV, purity@40
(analysis status update for Spin PWG)
Slides in pdf format:
For this post LDA results with Set1 and Set2 has been used
Note, that LDA for various preshower bins is trained independetly
pdflinks with results for pre1=0 and pre2=0 (preshower bin 1):
Figures below are for 0.004<preshower1<0.01 (preshower bin 4).
Photon pt cut: pt> 7, preshower bin: 0.004 < pre1 < 0.01
LDA cut with efficiency @ 70%
What is added in Set2 compared to Set1:
smaller cluster size information (r2x1, r2x2), postshower 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
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: photonjet 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 13: tower energy in r=0.7 radius
Figure 14: 3x3 preshower1 energy
Figure 15: 3x3 preshower2 energy
Figure 16: 3x3 postshower energy
Title:
"Photonjet coincidence measurements
in polarized pp collisions at sqrt{s}=200GeV
with the STAR Endcap Calorimeter"
Title:
"Photonjet coincidence measurements
in polarized pp collisions at sqrt{s}=200 GeV
with the STAR Endcap Calorimeter"
Data set and cuts:
Figure 1: Average ratio: pt_true / (pt_reco/1.3) vs. pt_reco (GeV/c)
Figure 2:
Average momentum difference: pt_true  (pt_reco/1.3) vs. pt_reco (GeV/c)
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 46 GeV range.
Before further pursuing our efforts in tuning the tower energy response in the MonteCarlo,
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
MonteCarlo setup:
Some definitions:
Notations used in the plots:
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
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
Figure 3a: SMD energy vs. energy thrown
Figure 3b: SMD energy vs. eta thrown
MonteCarlo setup is desribed here
Figure 1:Single photon shower shape before (red) and after (black) EEMC cAir bug fixed
pt=78GeV, eta=1.21.4 (left), eta=1.61.8 (right)
Figure 2: Single photon shower shape vs. data
MonteCarlo: pt=710GeV, eta=1.61.8
data: no preshower1,2; pt_photon>7, pt_jet>5. no eta cuts.
(see Fig. 1 from here for other preshower conditions)
MonteCarlo setup is desribed here
Color coding:
Single particle shower shape before (left) and after (right) EEMC cAir bug fixed
Single particle kinematic cuts: pt=78GeV, eta=1.21.4
Etameson shower shapes (blue) taken from Fig. 1 from here of this post
All shapes are normalized to 1 at peak (central strip).
Figure 1: Preshower bin 0: E_pre1=0; E_pre2=0
Figure 2: Preshower bin 1: E_pre1=0; E_pre2>0
Figure 3: Preshower bin 2: E_pre1>0; E_pre1<0.004
Figure 4: Preshower bin 3: E_pre1>0.004; E_pre1<0.01
Results only for corrected EEMC geometry
All shapes are divided by MC singlephoton shower shape.
Figure 5a: Preshower bin 0: E_pre1=0; E_pre2=0
Figure 5b: Preshower bin 1: E_pre1=0; E_pre2>0
Figure 5c: Preshower bin 2: E_pre1>0; E_pre1<0.004
Figure 5d: Preshower bin 3: E_pre1>0.004; E_pre1<0.01
Figure 6: Single photon to etameson shape ratios only (with error bars):
Preshower bins 0 (upperleft),1 (upperright),2 (lowerleft), and 3 (lowerright)
Real data, and signal/background MonteCarlo samples:
pp@200GeV collisions, STAR produnctionLong.
Trigger: eemchttpmbL2gamma [id:137641] (L ~ 3.164 pb^1)
Pythia prompt photon (signal) MonteCarlo sample.
Filtered Prompt Photon p6410EemcGammaFilter.
Partonic pt range 225 GeV.
Pythia 2>2 hard QCD processes (background) MonteCarlo sample.
Filtered QCD p6410EemcGammaFilter.
Partonic pt range 225 GeV.
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MonteCarlo setup:
Color coding:
Preshower bins:
Ep1/Ep2 is the energy deposited in the 1st/2nd EEMC preshower layer.
For a single particle MC it is a sum over
all preshower tiles in the EEMC with energy of 3 sigma above pedestal.
For etameson from pp2006 data the sum is over 3x3 tower patch
Single particle kinematic cuts: pt=78GeV, eta=1.21.4
Etameson 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 preshower conditions.
cAirFixed EEMC geometry (NO slow simulator, WITH SVT)
Ratio plot
Figure 2: Shower shape sorted by preshower conditions.
cAirFixed EEMC geometry (NO slow simulator, NO SVT)
Ratio plot
Figure 3: Shower shape sorted by preshower conditions.
cAirFixed EEMC geometry (WITH slow simulator, WITH SVT)
Ratio plot
Figure 4: Shower shape sorted by preshower conditions.
Old cAirbug EEMC geometry (NO slow simulator, WITH SVT)
Click here to see the plot
Starting with a fixed (50K events) for each type of particle.
Change in number of counts for a given preshower bin
with different detector configuration shows preshower migration
Figure 5: Preshower migration.
cAirFixed EEMC geometry (WITH SVT)
Figure 6: Preshower migration.
cAirFixed EEMC geometry (WITHOUT SVT)
Figure 7: Sampling fraction (0.05 E_reco / E_thrown).
cAirFixed EEMC geometry (WITHOUT Slowsimulator)
Figure 8: Sampling fraction (0.05 E_reco / E_thrown).
cAirFixed EEMC geometry (WITH Slowsimulator)
Slow simulator introduce some nonlinearity in the sampling fraction
Figure 9: Sampling fraction (0.05 E_reco / E_thrown).
cAirFixed EEMC geometry (WITHOUT SVT, WITHOUT Slowsimulator)
Click here to see the plot
Figure 10: Sampling fraction (0.05 E_reco / E_thrown).
Old cAirbug EEMC geometry (NO slow simulator, WITH SVT)
Click here to see the plot
MonteCarlo setup:
Color coding:
Preshower bins:
Note: Ep1/Ep2 is the energy deposited in the 1st/2nd EEMC preshower layer.
For a single photon MC it is a sum over
all preshower tiles in the EEMC with energy of 3 sigma above pedestal.
For etameson/gammajet candidates from pp2006 data the sum is over 3x3 tower patch
Single particle kinematic cuts: pt=78GeV, eta=1.21.4
Etameson 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 preshower 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)
Figure 3: Sampling fraction (0.05 * E_reco/ E_thrown)
Figure 4: Preshower1 energy (all tiles)
Figure 5: Preshower2 energy (all tiles)
Figure 6: Postshower energy (all tiles)
Figure 7: Preshower bin photon migration
Figure 8a: Energy ratio in 2x1 to 3x3 cluster
For the first 4 preshower bins total yield in MC is normalized to that of the data
Blue circles indicate photonjet 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 option for the STAR geometry (Low cuts on ElectroMagnetic processes)
is equivalent to the following set of GEANT cuts:
All these values are for kinetic energy in GeV.
Cut meaning and GEANT default values:
Some details can be found at this link and in the GEANT manual
MonteCarlo setup:
Color coding:
Figure 1: Sampling fraction vs. thrown energy (upper plot)
and vs. azimuthal angle (lower left) and rapidity (lower right)
Single particle kinematic cuts: pt=78GeV, eta=1.21.4
Etameson shower shapes (blue) taken from Fig. 1 from here of this post
All shapes are normalized to 1 at peak (central strip)
Preshower bins:
Ep1/Ep2 is the energy deposited in the 1st/2nd EEMC preshower layer.
For a single particle MC it is a sum over
all preshower tiles in the EEMC with energy of 3 sigma above pedestal.
For etameson from pp2006 data the sum is over 3x3 tower patch
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.
blue  added volumes in Jason file
red  G10 volume removed in Jason file
HITS  sensitive volumes
 Jason file 
ECAL
EAGA
EMSS
 EFLP
 ECVO
  EMOD
   ESEC
    ERAD
     ELED
    EMGT
     EPER
      ETAR
       EALP
       ESCI > HITS
 ESHM
  ESPL
   EXSG
    EXPS
    EHMS > HITS
    EBLS
    EFLS
  ERSM
 ESSP
 ERCM
 EPSB
ECGH
 ECHC
 CVS file 
ECAL
EAGA
EMSS
 EFLP
 ECVO
  EMOD
   ESEC
    ERAD
     ELED
    EMGT
     EPER
      ETAR
       EALP
       ESCI > HITS
 ESHM
  ESPL
   EXSG
    EHMS > HITS
   EXGT
  ERSM
 ESSP
 ERCM
 EPSB
ECGH
 ECHC
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=zslicecenter+zwidth/2 Create AND Position ECVO z=zslicecenter+zwidth/2 Create AND Position ESHM z=zslicecenter+zwidth/2 kOnly='MANY' Create AND Position ECVO z=zslicecenter+zwidth/2 Create AND Position ESSP z=zslicecenter+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=sectioncurr+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=xyz ncopy=isec kOnly='MANY' Create and Position EXSG alphaz=d3 ncopy=isec kOnly='MANY' Create and Position EXSG alphaz=d3 ort=xyz 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=xyz kOnly='ONLY' Create and Position EFLS x=xc y=yc z=(esmd_apex/2esmd_front_layer/2) alphaz=45 ort=xyz 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
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=zslicecenter+slcwid/2 Create and Position ECVO z=zslicecenter+slcwid/2 Create and Position ESHM z=zslicecenter+slcwid/2 Create and Position ECVO z=zslicecenter+slcwid/2 Create and Position ESSP z=zslicecenter+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=sectioncurr+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/2isec+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=XYZ 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=XYZ 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 25 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 0no, 1west 2east 3both 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 interplate gap in phi GapCel = 0.03/2 ! HALF of the radial intercell 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} ! 1V,2U,3cutV,4cutU * Fill EXSE ! Second SMD section JSect = 2 ! Section number Zshift = 0. ! Section width sectype = {0,2,1,0,2,3} ! 1V,2U,3cutV,4cutU * Fill EXSE ! Third SMD section JSect = 3 ! Section number Zshift = 1.215 ! Section width sectype = {1,0,2,1,0,2} ! 1V,2U,3cutV,4cutU * * 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_Rmshift2*emcs_smshift dphi = (emcs_PhiMaxemcs_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_Zendemcs_ZOrig)/2, Rmn1=orgkeep*Tan_Lowd2 Rmn2=endkeep*Tan_Lowd2, Rmx1=orgkeep*Tan_Upp+dup Rmx2=endkeep*Tan_Upp+dup do ihalf=1,2 filled=1 halfi = 105 + (ihalf1)*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_Zendemcs_ZOrig)/2, Rmn1=orgkeep*Tan_Lowd2 Rmn2=endkeep*Tan_Lowd2, 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_Zendemcs_ZOrig)/2, Rmn1=orgkeep*Tan_Lowd2 Rmn2=endkeep*Tan_Lowd2, 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=zslicecenter+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=zslicecenter+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=zslicecenter+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 = zslicecenter+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=zslicecenter+slcwid/2 zslice = zslice + slcwid prin1 zslice (' BackPlate ends at: ',F10.5) slcwid = emcs_Zendemcs_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_Zendemcs_ZOrig p = atan(xc/yc)/degrad Create EPSB do is = 1,6 xx = 75 + (is1)*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_Lowdd Rmn2=(zslice+slcwid)*Tan_Lowdd, 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_section1)*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_Lowdd, rmn2=(zslice+slcwid)*Tan_Lowdd, 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_Zendemcs_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_Lowd2 xleft = sqrt(xx*xx  rth*rth) yy=curr*Tan_Upp+dup xright = sqrt(yy*yy  rth*rth) secwid = yy  xx xx=curcl*Tan_Lowd2 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=(curclcurr)/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_Lowdd Rmn2=(zslice+slcwid)*Tan_Lowdd, 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_Zendemcs_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_Lowdd Rmn2=(zslice+slcwid)*Tan_Lowdd, 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=sectioncurr+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=(sectiondiff)*Tan_Lowdd, rmn2=(section+secwiddiff)*Tan_Lowdd, rmx1=(sectiondiff)*Tan_Upp+dup, rmx2=(section+secwiddiff)*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=(currentdiff)*Tan_Lowdd, rmn2=(current+mgtdiff)*Tan_Lowdd, rmx1=(currentdiff)*Tan_Upp+dup, rmx2=(current+mgtdiff)*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/2isec+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=(currentdiff)*Tan_Lowdd, rmn2=(current+mgtdiff)*Tan_Lowdd, rmx1=(currentdiff)*Tan_Upp+dup, rmx2=(current+mgtdiff)*Tan_Upp+dup * curcl = current+mgt/2 Do ie = 1,nint(eetr_NEta) EtaBot = eetr_EtaBin(ie) EtaTop = eetr_EtaBin(ie+1) RBot=(curcldiff)*Tanf(EtaBot) * if(Plate > 0) then ! Ordinary Sci layer RTop=min((curcldiff)*Tanf(EtaTop), _ ((currentdiff)*Tan_Upp+dup)) else ! last Sci layer in section RTop=min((curcldiff)*Tanf(EtaTop), _ ((currentdiff)*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=(RTopRBot)/2, dx1=RBot*xxemcs_GapCel/yy, dx2=RTop*xxemcs_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=(RTopRBot)/2emcs_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',9.6E6) * 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.e6) 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_Lowdd, rmn2=(current+cell)*Tan_Lowdd, 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=(zslicediff)*Tan_Upp+dup, Rmx2=(zslice + slcwiddiff)*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=(RTopRBot)/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_Low1.526, rmax=(sectionsecwid/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  (isec1)*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=XYZ 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=XYZ 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_Low1.526, rmax=(sectionsecwid/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_str1)*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  (pxc)*(pxc))) 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  (pxc)*(pxc))) 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 = xxyy 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=XYZ 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',9.6E6) * 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.e6) 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=(sectiondiff)*Tan_Low1.526, rmax=(section+msecwddiff)*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 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 25 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 0no, 1west 2east 3both 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 interplate gap in phi GapCel = 0.03/2 ! HALF of the radial intercell 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 interplate gap in phi GapCel = 0.03/2 ! HALF of the radial intercell 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 asbuilt 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 asbuilt 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 36 / Radiators 35 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 asbuilt 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 723 / Radiators 623 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 asbuilt 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 asbuilt 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} ! 1V,2U,3cutV,4cutU c Fill EXSE ! Second SMD section JSect = 2 ! Section number Zshift = 0. ! Section width sectype = {0,2,1,0,2,3} ! 1V,2U,3cutV,4cutU c Fill EXSE ! Third SMD section JSect = 3 ! Section number Zshift = 1.215 ! Section width sectype = {1,0,2,1,0,2} ! 1V,2U,3cutV,4cutU 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/EJ590B10HH%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/CompositeMaterials2430/fiberglass1.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_phimaxemcs_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 halfwheels 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_zendemcs_zorg)/2, rmn1=emcs_zorg*tan_lowd2, rmn2=emcs_zend*tan_lowd2, rmx1=emcs_zorg*tan_upp+dup, rmx2=emcs_zend*tan_upp+dup c c DO ihalf=1,2 c filled = 1 halfi = 105 + (ihalf1)*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 halfwheels. 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_zendemcs_zorg)/2, rmn1=emcs_zorg*tan_lowd2 rmn2=emcs_zend*tan_lowd2, 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 TIERODS PENETRATING ECVO c c Material Steel c Attribute EMSS seen=1 colo=1 ! BLACK Shape CONS dz=(emcs_zendemcs_zorg)/2, rmn1=emcs_zorg*tan_lowd2 rmn2=emcs_zend*tan_lowd2, 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=zslicecenter+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 13 positioned at: ', F12.4 ) c Create AND Position ECVO z=zslicecenter+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=zslicecenter+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, submm 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 45 positioned at: ', F12.4 ) c Create AND Position ECVO z=zslicecenter+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=zslicecenter+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_zendemcs_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_zendemcs_zorg p = atan(xc/yc)/degrad c Create EPSB DO i = 1,6 c xx = 75 + (i1)*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_lowdd, rmn2=(zslice+zwidth)*tan_lowdd, rmx1=zslice*tan_upp+dup, rmx2=(zslice+zwidth)*tan_upp+dup c c Loop over the SIX SECTORS in the current halfwheel. 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_sector1)*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_lowdd, 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_zendemcs_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_lowd2 xleft = sqrt(xx*xx  rth*rth) yy=curr*tan_upp+dup xright = sqrt(yy*yy  rth*rth) secwid = yy  xx xx=curcl*tan_lowd2 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=(curclcurr)/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_lowdd, rmn2=(zslice+zwidth)*tan_lowdd, 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_zendemcs_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_lowdd, rmn2=(zslice+zwidth)*tan_lowdd, 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, sectioncurr+secwid/2 ('+ ECVO isection=',I1,' zcenter=', F12.4) c Create AND Position ESEC z=sectioncurr+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_lowdd, rmn2=(section+secwid)*tan_lowdd, 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 ',