a) Statistics of Lambda and anti-Lambda Reconstruction, Inclusive

D_TT analysis Record, Rec_Step: all_cut_crp0995

Cuts Summary

Run 5

• standard spinDB requirements
• production triggers only
  

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

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

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

Run 6

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

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

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

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

Introduction at Spin PWG meeting - 5/10/07

Features

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

Included Analyses

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

Current Status

2006.07.31 First Look at SMD gamma/pi0 Discrimination

Pibero Djawotho

Indiana University
July 31, 2006

Simulation

Simulation were done by Jason for the SVT review.

Maximal side residual

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

xy distribution of SMD hits

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

Single peak characteristics

Fit function

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

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

Single event SMD response

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

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

Code

macros

Documents

1. Proposal to Contstruct an Endcap Calorimeter for Spin Physics at STAR
2. Appendix Simulation Studies of Direct Photon Production at STAR
3. An Endcap Calorimeter for STAR Conceptual Design Report
4. The STAR Endcap Electromagnetic Calorimeter (EEMC NIM)
5. An Endcap Calorimeter for STAR Technical Design Update #1
6. Jan's gamma/pi⁰ algorithm
7. Endcap Calorimeter Proposal (HTML @ IUCF)
8. STAR Note 401: An Endcap Electromagnetic Calorimeter for STAR--Conceptual Design Report
9. Spin Effects at Suppercollider Energies

2006.08.04 Second Look at SMD gamma/pi0 Discrimination

Second Look at SMD gamma/pi0 Discrimination

Pibero Djawotho
Indiana University
August 4, 2006

Dataset

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

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

Results

SMD U and V Fits

Code

macros

2006.08.06 Comparison between EEMC fast and slow simulator

Comparison between EEMC fast and slow simulator

Pibero Djawotho
Indiana University
August 6, 2006

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

The following settings were used in running the slow simulator:

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

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

EEMC Fast Simulator	EEMC Slow Simulator

2006.09.15 Fit Parameters

Fit Parameters

Fit Function

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

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

Simulation

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

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

Ditto in log scale.

Ditto by sector.

Fit Widths

Data from 2005 pp200 EEMC HT 1 and 2 triggers

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

Ditto in log scale.

Data from 2005 pp200 electrons

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

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

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

2007.02.05 Reconstructed/Monte Carlo Photon Energy

Reconstructed/Monte Carlo Photon Energy

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

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

• Fit to a constant



• Fit to a line



• Fit to a quadratic



• Comparison between Weihong's and Pibero's results

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

• Constant



• Linear



• Quadratic



• Conclusion

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

• References

1. M. Albrow et al., NIM A 480 (2002) 524-546.
2. R. Blair et al. (CDF Collaboration), CDF II Technical Design Report, FERMILAB-PUB-96-390-E, 1996.

---

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

2007.02.08 E_reco / E_mc vs. eta

E_reco / E_mc vs. eta

Legend

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

Jason's Monte Carlo

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

Will's Monte Carlo

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

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

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

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

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

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

Sampling fraction integrated over all eta's.

2007.02.11 Reconstructed/Monte Carlo Muon Energy

Reconstructed/Monte Carlo Muon Energy

10k muons thrown by Will with:

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

z_vertex

p_T vs. η

E_MC

E_tower/E_MC vs. η

E_cluster/E_MC vs. η

E_towertanh(eta) vs. eta

2007.02.15 160 GeV photons

160 GeV photons

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

2007.02.15 20 GeV photons

20 GeV photons

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

2007.02.15 80 GeV photons

80 GeV photons

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

2007.02.15 Reconstructed/Monte Carlo Electron Energy

Reconstructed/Monte Carlo Electron Energy

E=1 GeV	E=2 GeV

2007.02.19 10 GeV photons

10 GeV photons

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

2007.02.19 40 GeV photons

40 GeV photons

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

2007.02.19 5 GeV photons

5 GeV photons

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

2007.02.19 Summary of Reconstructed/Monte Carlo Photon Energy

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

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

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

entire

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

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

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

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

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

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

2007.05.24 gamma/pi0 separation in EEMC using linear cut

gamma/pi0 separation in EEMC using linear cut

2007.05.24 gamma/pi0 separation in EEMC using quadratic cut

gamma/pi0 separation in EEMC using quadratic cut

2007.05.24 gamma/pi0 separation in EEMC using quadratic cut

gamma/pi0 separation in EEMC using quadratic cut

2007.05.30 Efficiency of reconstructing photons in EEMC

Efficiency of reconstructing photons in EEMC

Monte Carlo sample

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

SMD gamma/pi0 discrimination algorithm

The following

slide

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

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

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

Optimizing cuts for gamma/pi0 separation

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

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

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

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

Hi Pibero,

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

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

Scott

Hi Pibero,

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

    y(x) = a + bx^2

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

More fun and games.

Scott

Hi Pibero,

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

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

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

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

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

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

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

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

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

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

Using this in the above general equation yields the cubic equation

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

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

    x^3 + px + q = 0

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

Anyway, it has been an interesting intellectual exercise!

Scott

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

Efficiency and Rejection

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

Rejection vs. efficiency at different energies

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

Rejection vs. efficiency with preshower cut

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

ALL ENERGIES
E=20-40 GeV
E=40-60 GeV
E=60-80 GeV
E=80-90 GeV

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

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

2007.06.28 Photons in Pythia

Pythia Simulations

 

Pythia Simulations

---

All partonic pT

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

pT=9-11 GeV

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

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

Number of prompt photons per event from GEANT record

---

Pibero Djawotho

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

a_LL

 

Partonic a_LL

Jet	Gamma

---

Pibero Djawotho

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

gamma pT=9-11 GeV

 

gamma pT=9-11 GeV

---

Pibero Djawotho

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

gamma-jet kinematics

 

gamma-jet kinematics

---

Clusters without parent track

---

---

Pibero Djawotho

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

gamma/X separation by energy

 

gamma/X separation by energy

---

Pibero Djawotho

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

gamma/X separation by energy with pT weights

 

gamma/X separation by energy with pT weights

---

Pibero Djawotho

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

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

 

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

---

Pibero Djawotho

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

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

 

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

---

---

Pibero Djawotho

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

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

 

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

---

---

Pibero Djawotho

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

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

 

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

---

---

Pibero Djawotho

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

2007.07.09 How to run the gamma fitter

How to run the gamma fitter

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

root4star StRoot/StGammaMaker/macros/RunGammaFitterDemo.C

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

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

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

Description

The class

StGammaFitter

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

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

Code

• StGammaFitter.h
• StGammaFitter.cxx
• StGammaFitterResult.h
• StGammaFitterResult.cxx
• ReadGammaTree2.C

Candidates selection

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

Raw SMD response

1. No additional cuts
2. Pick only L2-gamma triggers
3. Pick only L2-gamma triggers but no jet patch trigger
4. Make isolation cut (see below)

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

2007.09.12 Endcap Electrons

Endcap Electrons

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

• Transverse running

• Analysis uses 64 out of 300 runs from 2006 pp transverse run
• MuDst are located at:

  /star/institutions/iucf/balewski/prodOfficial06_muDst/

• No trigger selection


Figure 1: Number of tracks surviving each successive cut



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

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



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

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



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



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



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



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



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



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



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



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



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



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



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



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

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


• Longitudinal running

• MuDst are located at:

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



Figure 2.1



Figure 2.2: The dominant trigger ids are:

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



Figure 2.3a



Figure 2.3



Figure 2.4



Figure 2.5



Figure 2.6



Figure 2.7



Figure 2.8



Figure 2.9



Figure 2.10

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

• Code

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

• Summed SMD response

SMD response function

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

• p0 = yield

• p1 = centroid

Transverse

21 electrons



Longitudinal

99 electrons

2008.01.23 Endcap etas

Endcap etas

Endcap etas

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

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

Additional Material

• List of etas from Weihong (text file)
• List of etas from Weihong (pdf file)
• List of etas from Weihong using gammma maker
• List of etas from Weihong (all 12 sectors)
• Hal's eta spreadsheet
• Hal's revised eta spreadsheet

Documents

• Direct photon center-of-mass angular distributions in proton-antiproton collisions at sqrt(s)=1.8 TeV (Leslie Nakae Thesis)
• Measurement of the cross section for production of two isolated prompt photons in 1.8-TeV proton-antiproton collisions (Mikio Takano Thesis)
• Measurement of sqrt(s) dependence of isolated direct photon production in proton-antiproton collisions (Dana Partos)
• Prompt photon cross section measurement in pbar-p collisions at sqrt(s)=1.8 TeV (PRD 48 2998, 1993)
• A prompt photon cross section measurement in pbar-p collisions at sqrt(s)=1.8 TeV (FERMILAB-Pub-92/001-E)

2008.02.27 ESMD shape library

ESMD shape library

Shower Widths for Monte Carlo and Data

Description

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

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

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

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

• Monte Carlo U shower shape
• Monte Carlo V shower shape
• SMDU shower shape (Will's selection)
• SMDV shower shape (Will's selection)
• Energy Asymmetry
• Index of Library Entry

Decsription of Pythia Sample

A few histograms were added to the code:

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

Figure 1:

Data vs. MC mean u-strip

Figure 2:

Data vs. MC mean v-strip

Figure 3:

Data vs. MC u-strip sigma

Figure 4:

Data vs. MC v-strip sigma

Figure 5:

MC E

_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

esmd.pdf

or

esmd_solid.pdf

.

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

2008.02.28 ESMD QA for run 7136033

ESMD QA for run 7136033

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

A second look at eta mesons in the STAR Endcap Calorimeter

Introduction

In case you missed it, the first look is

here

. I processed

44 runs

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

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

• Eta.C
• StEtaFinder.h
• StEtaFinder.cxx
• PlotEtas.C
• ETAS.root

Note, the workhorse function is

.

Algorithm

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

Invariant mass

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

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

Additional plots

• PhotonsFromEtas.txt
• PhotonsFromEtas2.txt

---

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

2008.03.08 Adding the SMD energy to E_reco/E_MC for Photons

Adding the SMD energy to E_reco/E_MC for Photons

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

QA plots for each energy

1. 5 GeV
2. 10 GeV
3. 20 GeV
4. 40 GeV
5. 80 GeV
6. 160 GeV

E_SMD/E_reco vs. eta

2008.03.21 Chi square method

Chi square method

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

2008.04.08 Data-Driven Shower Shapes

Data-Driven Shower Shapes

Gamma Conversion before the Endcap

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

Shower shapes comparison

This

PDF

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

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

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

These Shower Shapes are binned by:

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

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

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

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

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

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

All fits to the data are with reference to a single

Shower Shape

. The fit function is:

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

1. All Shower Shapes
2. No Conversion
3. Conversion
4. No Preshower
5. Preshower
6. No Conversion and Preshower
7. Sector Configuration 0
8. Sector Configuration 1
9. Sector Configuration 2

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

1. All Shower Shapes
2. No Conversion
3. Conversion
4. No Preshower
5. Preshower
6. No Conversion and Preshower
7. Sector Configuration 0
8. Sector Configuration 1
9. Sector Configuration 2

2008.04.12 Data-Driven Residuals

Data-Driven Residuals

Gammas

Jets

Background Rejection vs. Signal Efficiency

Partonic pT=9-11 GeV	Partonic pT=9-11 GeV

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

2008.04.12 Pythia Gamma-Jets

Pythia Gamma-Jets

Gamma-Jet Yields

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

Gamma-Jets p_T slope

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

References

1. Yield estimates based on single-particle MC sample, and comparison w/ pythia (Jason Webb)
2. Pythia estimates of gamma-jet yields (Jim Sowinski)

2008.04.16 Jet Finder QA

Jet Finder QA

2008.04.20 BUR 2009

Partonic pT=7-9 GeV

Partonic pT=9-11 GeV

Combined Partonic pT

2008.04.22 Run 6 Photon Yield Per Trigger

Run 6 Photon Yield Per Trigger

Introduction

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

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

Event selection

Trigger selection

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

Gamma candidates

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

StGammaMaker

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

.

Track isolation

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

EMC isolation

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

_T^gamma

/E

_T^cone

> 0.85 and R=√Δη

²

+Δφ

²

=0.3 is the cone radius.

Jet Reconstruction

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

_gamma

-φ

_jet

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

. A local mirror exists at RCF under the directory

.

Spin Information

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

Event Summary

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

Gamma-Jet Plots

Comparison of pT Slope with Pythia

Partonic Kinematics Reconstruction

Open Questions

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

2008.05.07 Number of Jets

Number of Jets

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

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

2008.05.09 Gamma-jets pT distributions

Gamma-jets pT distributions

Note:

No cuts on residuals applied.

Not cut on number of towers in gamma cluster

Number of towers in gamma cluster <= 9

Number of towers in gamma cluster <= 4

Number of towers per cluster distributions

Gamma candidates xy-distribution

z-vertex distribution

Eta distribution

Phi distribution

log10(E_post/E_tow) distribution

pT asymmetry

References

1. Ilya's pT distributions
2. Michael's weigthing of simulation

2008.05.19 Binning the shower shape library

Binning the shower shape library

Distributions

Shower Shapes

2008.06.03 Jet A_LL Systematics

Jet A_LL Systematics

Hypernews discussion

jet A_LL systematic possibility

References

1. I.P. Auer et al, Phys.Rev.D 32(1985)1609
2. J. Bystricky et al, J.Phys. France 39(1978)1

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

2008.07.16 Extracting A_LL and DeltaG

Extracting A_LL and DeltaG

Determining state of beam polarization for Monte Carlo events

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

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

_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 parton-level asymmetry:

The algorithm then consists in alternatively drawing a random value N

_int

from the Poisson distributions with mean μ

₊

and μ

_-

until N

_int

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

_LL

=0.2 was fixed and N

_trials

=500 different asymmetries were calculated. Each trial integrated N

_total

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

_LL

~0.2 and the statistical precision~0.1:

Indeed, both the A

_LL

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

_total

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

_LL

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

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

Figure 1a	Figure 1b

Event reconstruction

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

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

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

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

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Partonic kinematics reconstruction

PartonicKinematics.C

Figure 10

Figure 11

Figure 12

Figure 13

Figure 14

Figure 15 (a)

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

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

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

Predictions for A_LL and direct determination of DeltaG(x)

DeltaG.C

Figure 16 (a)

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

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

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

References

1. Appendix Simulation Studies of Direct Photon Production at STAR
2. DeltaG(x,mu^2) from jet and prompt photon production at RHIC arXiv:hep-ph/0005320

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

How to install Pythia 6 and 8 on your laptop?

• Install Pythia 6 and build the interface to ROOT

Download the file pythia6.tar.gz from the ROOT site ftp://root.cern.ch/root/pythia6.tar.gz and unpack.

tar zxvf pythia6.tar.gz

A 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.linux

For more information, consult Installing ROOT from Source and skip to the section Pythia Event Generators.

• Install Pythia 8

Download the latest version of Pythia from http://home.thep.lu.se/~torbjorn/Pythia.html and unpack.

tar zxvf pythia8108.tgz

A 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/xmldoc

Run configure with the option for shared-library creation turned on.

./configure --enable-shared
make

• Install ROOT from source

Download the source code for ROOT from http://root.cern.ch/ and compile.

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

Set 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/man

You 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

2008.07.23 Hot Strips Identified by Hal Spinka

Hot Strips Identified by Hal Spinka

• Run 7136034 Sector 8



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



• Run 7137036 Sector 9



Strips 09V064



---

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

2008.07.24 Strips from Weihong's 2006 ppLong 20 runs

Strips from Weihong's 2006 ppLong 20 runs

Energy [GeV] vs. strip id

2006ppLongRuns.pdf

Raw ADC vs. strip id

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

Invariant Mass and Width: Data-MC

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

The Method:

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

The results are shown in the two figures below.

Mass:

Width:

Neutral Pion Paper: 2005 ALL & <z>

Neutral Pion Paper for 2005 data: Final Results.

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

Final result for A_LL:

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

The chi2/ndf for the different model curves are:

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

Final Result for <z>:

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

Below are links to details about the two results.

Data used

Data used in analysis

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

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

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

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

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

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

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

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

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

Double spin asymmetry

Double spin asymmetry

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 P₁ and P₂ 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 fill-by-fill measurement of A_TT, corrected by the beam polarisation, summed over all p_T.

Figure 1: K0S ATT fill-by-fill

Figure 2: Λ ATT fill-by-fill

Energy loss identification

Energy loss particle identification

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

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

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 anti-Λ hypothesis passing dE/dx cut

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

Table 1

Geometrical cuts

Geometrical cuts

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

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

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

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

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

Figure 1: Final K0S mass spectrum with all cuts applied.

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

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

Single spin asymmetry using cross formula

Single Spin asymmetry using cross formula

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

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.)

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

Figure 1a: K0S blue beam asymmetry

Figure 1b: K0S 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: K0S pT-dependent blue beam AN

Figure 3b: K0S pT-dependent yellow beam AN

Figure 4a: Λ pT-dependent blue beam AN

Figure 4b: Λ pT-dependent yellow beam AN

Single spin asymmetry utilising relative luminosity

Single spin asymmetry making use of relative luminosity

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

Equation 1

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

Equation 2

Equation 3

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

Figure 1a: Blue beam asymmetry for K0S

Figure 1b: Yellow beam asymmetry for K0S

Figure 2a: Blue beam asymmetry for Λ

Figure 2b: Yellow beam asymmetry for Λ

V0 decays

V0 decays

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

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

Figure 1: Invariant mass spectrum under K0s hypothesis

Figure 2: Invariant mass spectrum under Λ hypothesis

Figure 3: Invariant mass spectrum under anti-Λ hypothesis

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

2008

Year 2008 posts