# Spin

STAR Spin Working Group

Unraveling the quark and gluon substructure of nucleons and nuclei is one of the major goals in nuclear physics today. A great deal has been learned about the partonic structure of the nucleon at leading twist and with collinear factorization, but much is still unknown. Furthermore, new avenues have been opened during the past decade to explore the nucleon structure beyond leading twist and collinear factorization. The ability to collide polarized beams at RHIC provides unique information regarding these issues.

- STAR Decadal Plan (2010)

# Spin PWG

This is a feed of Drupal items targeting the "Spin" Audience.

# Physics Analysis

Detailed information about physics analyses in the spin pwg

Link to STAR spin task force (2008)

(page started in March of 2008)

# 2006 EEMC Neutral Pion Cross Section and A_LL

The 2006 EEMC cross section, ALL, and AN were published in Physical Review D 89, 012001 (2014). Please, see the paper home page for links to detailed information.

# Intent to Publish 2006 Gamma + Jet Cross Section

## Title

Gamma-jet cross sections for forward gammas in proton-proton collisions at root(s) = 200 GeV/c

## Principal Authors (alphabetical order)

Keith Krueger (ANL), Hal Spinka (ANL), Dave Underwood (ANL)

## Intended Journal

Physical Review D

## Abstract

A measurement is presented of the cross section vs. transverse momentum (pT) for gamma + jet production in proton-proton collisions. The data were measured in the STAR detector at RHIC at √s = 200 GeV/c. The jet was detected at central pseudorapidity (|η| < 0.8) and the γ at intermediate pseudorapidity (1.2 < η < 2.0). These regions were chosen to access lower x of the gluon relative to a central-pseudorapidity-only measurement, and also because a large partonic spin asymmetry, All, in the parton cm is selected. The technique of finding single γ’s in the background of photons from π0 decay is based on a standard chi-squared method for the shower shape in the shower maximum detector of an electromagnetic calorimeter.

## Outline

• Introduction
• Refs. to earlier measurements and theory predictions
• Connection to gluon distribution
• Hardware
• RHIC general (Refs.)
• STAR general (Refs.)
• TPC and BEMC for jets (Refs.)
• EEMC and ESMD for gamma
• trigger
• luminosity
• Analysis
• Jets (Refs.)
• Gammas with chi-squared (Ref.)
• Efficiency / RooUnfold
• Other Corrections?
• Systematics
• Table of results with errors
• Results
• Comparison to JETPHOX (Refs.)
• Comparison to pi0’s?
• Summary / Conclusions

# 2009 Lambda D_LL @ 200 GeV

Presentations:

Run QA by Qinghua Xu (SDU)

# 1) Preliminary results

Preliminary released on SPIN 2012 and DNP 2012.

Presentation @ SPIN 2012 by Jian Deng (SDU)
Presentation @ DNP 2012 by Ramon Cendejas (UCLA)

# 2) Lambda reconstruction

1. Cuts setup:
 lam_pt dac2 dcaV0 dca_p dca_pi nsigma dlength cosrp jet_det_eta jet_Rt jet_dr 2,3 <0.7 <1.2 >0.2 (0.4,30) <3 (3,130) >0.98 (-0.7,0.7) (0.01,0.94) <0.7 3,4 <0.5 <1.2 0 (0.4,30) <3 (3.5,130) >0.98 (-0.7,0.7) (0.01,0.94) <0.7 4,5 <0.5 <1.2 0 (0.4,30) <3 (4,130) >0.98 (-0.7,0.7) (0.01,0.94) <0.7 5,8 <0.5 <1.2 0 (0.4,30) <3 (4.5,130) >0.98 (-0.7,0.7) (0.01,0.94) <0.7

2. Analysis plots:

cos\theta^* vs mass by pT .pdf
slope band from K0-short: .pdf

3. Invariant Mass distribution and background estimation, side band vs. fitting

All 4 lambda pT bins, fired jet only .pdf .txt no fired jet required .pdf .txt

4. Extract Lambda and Anti-Lambda yields for 20 cos \theta^* , for 4 spin status, for JP1 and L2JetHigh triggers.
 Lambda pT Lambda JP1 AntiLambda JP1 Lambda L2JetHigh AntiLambda L2JetHigh 2, 3 FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html 3, 4 FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html 4, 5 FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html 5, 8 FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html

5. Extract DLL from the spin sorted Lambda/AntiLambda yeilds in each cos \theta^* bin, for each trigger
 Lambda pT Lambda JP1 AntiLambda JP1 Lambda L2JetHigh AntiLambda L2JetHigh 2, 3 FiredJet .html  noFire .html FiredJet .html  noFire .html FiredJet .html  noFire .html FiredJet .html  noFire .html 3, 4 FiredJet .html  noFire .html FiredJet .html  noFire .html FiredJet .html  noFire .html FiredJet .html  noFire .html 4, 5 FiredJet .html  noFire .html FiredJet .html  noFire .html FiredJet .html  noFire .html FiredJet .html  noFire .html 5, 8 FiredJet .html  noFire .html FiredJet .html  noFire .html FiredJet .html  noFire .html FiredJet .html  noFire .html

6. Fit DLL vs. cos \theta^* to extract the "DLL"

 Lambda pT Lambda JP1 AntiLambda JP1 Lambda L2JetHigh AntiLambda L2JetHigh 2, 3 FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html 3, 4 FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html 4, 5 FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html 5, 8 FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html FiredJet.pdf .txt .html  noFire .pdf .txt .html

7. DLL correction

Appendix,  Anti-Lambda/Lambda ratio:

# 3) New simulation production

Background
Simulations performed by Ramon Cendejas. Unfortunately, an error was found in the (subdominant) process contributions when Ramon needed to move on. This and other factors held up progress since.

Introduction
• Pure MC simulation under STAR geometry.
• Generate pp events using Pythia 6.4.28 (Tune 320) with Lambda filter, in different partonic pT intervals and weighted by integral luminosities
• Reconstruct Lambda based on track association
• Jet reconstruction used CDF cone algorithm with R = 0.7

Statistics of simulation sample
It totally took about 4.5 CPU years.

Slides20170110

Jet Cone Study
1. slides (JP1 lambda pt2-3 for example)
2. All comparison plots for different lambda pt bin and different Triggers

Trigger Bias Plot
1. updated version for cdf cone algorigthm

Data/MC comparison for
Lambda:

 lamdba pt JP1 L2JetHigh 2_3 file file 3_4 file file 4_5 file file 5_8 file file
Anti-lambda:
 A-lamdba pt JP1 L2JetHigh 2_3 file file 3_4 file file 4_5 file file 5_8 file file

QA:
 lamdba pt MB JP1 L2JetHigh 2_3 file file file 3_4 file file file 4_5 file file file 5_8 file file file

Trigger effect: (old, anti-kt R06 )
 f_z feed down parton subprocess Lambda file1 file2 file file file A-lambda file1 file2 file file file

# 4) Systematic uncertainties

Systematic uncertainties summary

• decay, fz, and f_parton from simulation
• pile-up and residual background are from preliminary version

 η > 0 η < 0 Lambda Anti-lambda Lambda Anti-lambda pt JP1 L2J JP1 L2J JP1 L2J JP1 L2J decay 2.4 0.0009 0.0018 0.0001 0.0003 0.0001 0 0.0001 0.0001 3.4 0.0008 0.0011 0.0004 0.0003 0 0.0001 0 0 4.4 0.002 0.0027 0.0008 0.0008 0.0003 0.0004 0.0001 0.0002 5.9 0.0017 0.0024 0.0013 0.0022 0.0005 0.0009 0.0003 0.0007 fz 2.4 0.0011 0.0028 0.0003 0.0015 0.0002 0.0001 0.0001 0.0001 3.4 0.001 0.0034 0.0006 0.0024 0.0003 0.0005 0.0003 0 4.4 0.0056 0.008 0.0053 0.0079 0.0005 0.0007 0.0009 0.0013 5.9 0.009 0.0121 0.0154 0.0191 0.0021 0.0028 0.0037 0.0046 fparton 2.4 0.0005 0.0008 0.0004 0.0012 0.0001 0.0001 0.0001 0.0002 3.4 0.0011 0.0021 0.0002 0.0013 0.0003 0.0005 0 0.0003 4.4 0.0019 0.0038 0.0003 0.0011 0.0005 0.0009 0.0001 0.0003 5.9 0.0034 0.0062 0.0016 0.0027 0.0011 0.002 0.0005 0.0009 pile-up 2.4 0.0182 0.0057 0.0184 0.0063 0.0182 0.0057 0.0184 0.0063 3.4 0.0023 0.0007 0.0022 0.001 0.0023 0.0007 0.0022 0.001 4.4 0.0023 0.0007 0.0022 0.001 0.0023 0.0007 0.0022 0.001 5.9 0.0068 0.0023 0.0064 0.0023 0.0068 0.0023 0.0064 0.0023 bkgd 2.4 0.005 0.001 0.0001 0.0002 0.005 0.001 0.0001 0.0002 3.4 1.00E-06 0.0001 0.0001 0.0006 1.00E-06 0.0001 0.0001 0.0006 4.4 0.0007 4.00E-05 0.0004 0.0002 0.0007 4.00E-05 0.0004 0.0002 5.9 0.0002 0.0002 0.001 0.0001 0.0002 0.0002 0.001 0.0001 all 2.4 0.0189 0.0067 0.0184 0.0066 0.0189 0.0058 0.0184 0.0063 3.4 0.0029 0.0042 0.0023 0.0030 0.0023 0.0010 0.0022 0.0012 4.4 0.0067 0.0093 0.0058 0.0081 0.0025 0.0014 0.0024 0.0017 5.9 0.0119 0.0140 0.0168 0.0196 0.0072 0.0042 0.0075 0.0053

# 5) Paper proposal

Title:
Improved measurement of the longitudinal spin transfer to $\Lambda$ and $\bar{\Lambda}$
hyperons in polarized proton-proton collisions at $\sqrt{ s}$ = 200 GeV at RHIC

PAs:

Ramon Cendejas, Jian Deng, Jincheng Mei, Ernst Sichtermann, Qinghua Xu, and Jinlong Zhang

Proposed Target Journal:
PRD

Abstract:

The longitudinal spin transfer $D_{LL}$ of $\Lambda$ and $\bar{\Lambda}$ hyperons is expected to be sensitive to the helicity distribution function of strange quarks and anti-quarks, and to the longitudinally polarized fragmentation functions. We report an improved measurement on the longitudinal spin transfer of $\Lambda$ and $\bar{\Lambda}$ hyperons in proton-proton collisions at $\sqrt{s}$ = 200 GeV with the STAR detector at RHIC. The data are based on an approximately twelve times larger than that for our previously reported measurement and cover a kinematic range of $|\eta|<$ 1.2 in pseudo-rapidity and cover transverse momentum $p_T$ up to 6 GeV/c.  The dependences on $\eta$ and $p_T$ are presented and compared with model evaluations.

Figures:

Fig.1

Caption:
a) The invariant mass distribution for $\Lambda$ (red filled circles) and $\bar{\Lambda}$ (blue open circles) candidates with 3 $< p_T <$ 4 GeV/c in this analysis and b) the corresponding distribution versus the hyperon rest-frame angle $cos\theta^*$.

Fig.2

Caption:
The raw spin transfer $D _{LL}^{raw}$ versus cos for a) $\Lambda$ and b) $\bar{\Lambda}$ hyperons, and c) the spin asymmetry $\epsilon_{LL}$ for the control sample of $K_S^0$ mesons versus cos/theta  in the $p_T$ bin of (3,4) GeV/c for JP1 triggered sample. The red filled circles show the results for positive pseudorapidity $\eta$ with respect to the polarized beam and the blue open circles show the results for negative $\eta$. Only statistical uncertainties are shown.

Fig.3

Caption:
Comparison of spin transfer $D_{LL}$ for  for positive and negative $\eta$ versus $p_T$ for differently triggered samples in the present analysis, together with previously published results in the region of kinematic overlap. The results obtained with the L2JetHigh trigger have been offset to slightly larger $p_T$ values for clarity. The published results have been offset to slightly smaller $p_T$ values.

Fig.4

Caption:  Comparison of  spin transfer $D_{LL}$ with model predictions for positive $\eta$ versus $p_T$. The vertical bars and boxes indicate the sizes of the statistical and systematic uncertainties, respectively. The $\bar{\Lambda}$ results have been offset to slightly larger pT values for clarity.

Tables:
Tab 1

Caption: Summary of the selection cuts for \lla\ reconstruction for run9 Jet-Patch triggered sample and the corresponding $\Lambda$ ($\bar{\Lambda}$) counts and background fraction . Here DCA'' denotes distance of closest approach'' (to the primary vertex for single track by default) , and $\vec r$ denotes the vector from primary vertex to the V0 vertex and $\vec p$ denotes the momentum vector of V0.

Conclusion:

In summary, we report an improved measurement of the longitudinal spin transfer, $D_{LL}$, to $\Lambda$ hyperons and $\bar{\Lambda}$ anti-hyperons in longitudinally polarized proton-proton collisions at $\sqrt{s}$ = 200 GeV.  The data correspond to 19 pb$^{-1}$ with an average beam polarization of 57\% and were obtained with the STAR experiment in the year 2009.  The data cover -1.2 $<\eta<$ 1.2 and $p_T$ up to 6 GeV/c.  The longitudinal spin transfer is found to be $D_{LL}$ = -0.036 $\pm$ 0.048 (stat) $\pm$ 0.014(sys) for $\Lambda$ hyperons and $D_{LL}$ = 0.032 $\pm$ 0.043(stat) $\pm$ 0.019 (sys) for $\bar{\Lambda}$ anti-hyperons produced with $<$$\eta$$>$ = 0.5 and $<$$p_T$$>$ = 5.9 GeV/c.  While the data do not provide conclusive evidence for a spin transfer signal, the data tend to be below a model evaluation (DSV seen. 3) based on the extreme assumption that the quark polarized fragmentation functions are flavor-independent.

Paper draft and review:

Previous Publication:

Run 2005 DLL (PRD(R)) (comparison between run 9 and published run 5 data  points .pdf

Documentation:

Presentations:

• Preliminary result release at SPIN2012, DNP2013 (comparison between preliminary and final results .png)
• Nov 10, 2016 Analysis Meeting @LBL (link)
• May 16, 2017 Collaboration Meeting @BNL (link)
• Oct 16, 2017 SPIN PWG (link) (comments)
• Nov 10, 2017 PWGC review (link) (significance)
• Jan 25, 2018 Collaboration Meeting @LBL (link)

Analysis Note and Thesis:

Analysis Code:

• Code in Protected Area: link
• Code in CVS: link

hallo Anselm

# What Is Measured?

• The azimuthal transverse single-spin asymmetries in the production of jets and of pions within jets from p+p collisions at 500 GeV
• Inclusive jet azimuthal asymmetry
• A left-right asymmetry, relative to the beam spin-polarization, in the production of jets with a transverse momentum reconstructed with 5 < pT < 55 GeV/c
• Sensitive to twist-3 parton distribution functions (with multi-parton correlators)
• The twist-3 PDFs are sensitive to the kT-integrated Sivers function
• At these kinematics, in particular at low-pT, the data may provide new constraints on gluonic effects, e.g. gluon Sivers function
• "Collins" asymmetry
• A left-right asymmetry, relative to the scattered-quark spin-polarization, in the distribution of pions around the axis of their parent jet
• Sensitive to
• "Transversity": A fundamental leading-twist distribution function describing the transverse polarization of quarks within a transversely polarized proton
• "Collins" fragmentation function: A fragmentation function that is dependent upon the quark polarization and the transverse momentum of pions relative to the jet axis (here denoted "jT")
• Expected to have sensitivity only at high-pT, where quark-based subprocesses dominate
• "Collins-like" asymmetry
• An asymmetry, relative to the scattered gluon spin-polarization, in the distribution of pions around the axis of their parent jet
• Sensitive to
• Gluon linear polarization: The gluon analog of transversity
• "Collins-like" fragmentation function: Gluon analog of the Collins fragmentation function
• Expected to have sensitivity only at low-pT, where gluonic subprocesses dominate
• This is the first-ever measurement of this asymmetry and should provide the first constraints on gluon linear polarization

# Journal

Physical Review D

# Paper Title

Azimuthal transverse single-spin asymmetries of inclusive jets and charged-pions within jets from polarized-proton collisions at √s = 500 GeV

# Paper Abstract

We report the first measurements of transverse single-spin asymmetries for inclusive jet and jet + π± production at midrapidity from transversely polarized proton-proton collisions at √s = 500 GeV. The data were collected in 2011 with the STAR detector sampled from 23 pb-1 integrated luminosity with an average beam polarization of 53%. Asymmetries are reported for jets with transverse momenta 6 < pT, jet < 55 GeV/c and pseudorapidity |η| < 1. Presented are measurements of the inclusive-jet azimuthal transverse single-spin asymmetry, sensitive to twist-3 initial-state quark-gluon correlators; the Collins asymmetry, sensitive to quark transversity coupled to the polarized Collins fragmentation function; and the first measurement of the "Collins-like" asymmetry, sensitive to linearly polarized gluons. Within the present statistical precision, inclusive-jet and Collins-like asymmetries are small, with the latter allowing the first experimental constraints on gluon linear polarization in a polarized proton. At higher values of jet transverse momenta, we observe the first non-zero Collins asymmetries in polarized-proton collisions, with a statistical significance of greater than 5σ. The results span a range of x similar to results from SIDIS but at much higher Q2. The Collins results enable tests of universality and factorization-breaking in the transverse momentum-dependent formulation of perturbative quantum chromodynamics.

# Principal Authors

Jim Drachenberg1, Kevin Adkins3, Renee Fatemi3, Carl Gagliardi2, Adam Gibson4

1. Lamar University
2. Texas A&M University
3. University of Kentucky
4. Valparaiso University

# Money Plots

## Figure 1: Inclusive Jet Azimuthal Asymmetry

Figure 1: The inclusive-jet azimuthal transverse single-spin asymmetry as a function of jet pT in bins of jet pseudorapidity, measured relative to the polarized beam. Jet pT is shown corrected to the "particle-jet" level. A dashed line at zero is provided to guide the eye. Statistical uncertainties are shown as error bars while shaded boxes represent systematic uncertainties. An overall scale systematic of 3.5% for beam polarization uncertainty is not shown. The asymmetries are observed to be small and consistent with zero, at the current precision, over the full range of jet pT and jet pseudorapidity, suggesting a contribution below the current level of sensitivity from the twist-3 PDF at the present kinematics.

## Figure 2: Collins-like Asymmetry vs. z for Low pT, jet and vs. pT for Low z

Figure 2: Collins-like asymmetries as a function of particle-jet pT for pions reconstructed with 0.1 < z < 0.3 (left) and as a function of pion z for jets reconstructed with 6 < pT < 13.8 GeV/c (right). Asymmetries are shown combining π+ and π and integrating over the full range of jet pseudorapidity, −1 < η < 1. Statistical uncertainties are shown as error bars, while systematic uncertainties are shown as shaded error boxes. An additional 3.5% vertical scale uncertainty from polarization is correlated across all bins. Shaded bands represent maximal predictions utilizing two sets of fragmentation functions. The asymmetries are consistently small across the full range of jet pT and pion z and provide the first experimental constraints on model calculations.

## Figure 3: Collins(-like) Asymmetry vs. z

Figure 3: The (left) "Collins" and (right) "Collins-like" asymmetries are shown as a function of charged-pion z for two bins of jet psuedorapidity and three bins of jet pT. Asymmetries are shown separately for pion species. A dashed line at zero is provided to guide the eye. Statistical uncertainties are shown as error bars while shaded boxes represent systematic uncertainties. An overall scale systematic of 3.5% for beam polarization uncertainty is not shown. All Collins-like asymmetries are observed to be small and consistent with zero, at the current precision, over the full range of kinematics. The Collins asymmetries exhibit an asymmetry of 5σ signficance at high jet pT.

## Figure 4: Collins(-like) Asymmetry vs. pT

Figure 4: The (left) "Collins" and (right) "Collins-like" asymmetries are shown as a function of jet pT for two bins of jet psuedorapidity and three bins of charged-pion z. Asymmetries are shown separately for pion species. A dashed line at zero is provided to guide the eye. Statistical uncertainties are shown as error bars while shaded boxes represent systematic uncertainties. An overall scale systematic of 3.5% for beam polarization uncertainty is not shown. Collins-like asymmetries are observed to be small and consistent with zero, at the current precision, over the full range of kinematics. Collins asymmetries are non-zero for η > 0 beginning at higher jet pT, where quark-based subprocesses are expected to begin to play a significant role in the underlying partonic cross section.

## Figure 5: Collins Asymmetry vs. jT

Figure 5: The "Collins" asymmetries are shown as a function of jT for two bins of jet psuedorapidity and three bins of charged-pion z. Asymmetries are shown separately for pion species. A dashed line at zero is provided to guide the eye. Statistical uncertainties are shown as error bars while shaded boxes represent systematic uncertainties. An overall scale systematic of 3.5% for beam polarization uncertainty is not shown. Collins asymmetries are non-zero for η > 0 and tend to exhibit the largest effects at lower values of jT, e.g. ~0.3 GeV/c.

## Figure 6: Collins Asymmetry Model Comparison

Figure 6: Collins asymmetries as a function of pion z for jets reconstructed with 22.7 < pT < 55 GeV/c and 0 < η < 1. The asymmetries are shown in comparison with model calculations. The calculations are based upon SIDIS and e+e results and assume robust factorization and universality of the Collins function. The 2013 Fit and KPRY predictions assume no TMD evolution, while the KPRY-NLL curves assume TMD evolution up to next-to-leading-log. All predictions are shown with shaded bands corresponding to the size of their associated theoretical uncertainties. The general agreement between the data and the model calculations is consistent with assumptions of robust TMD-factorization and universality of the Collins function.

# Concluding Paragraph

We have reported the first measurements of transverse single-spin asymmetries from inclusive jet and jet + π± production in the central pseudorapidity range from p + p at √s = 500 GeV. The data were collected in 2011 with the STAR detector. As in previous measurements at 200 GeV, the inclusive jet asymmetry is consistent with zero at the available precision. The first-ever measurement of the "Collins-like" asymmetry, sensitive to linearly polarized gluons in a polarized proton, is found to be small and provide the first constraints on model calculations. For the first time, we observe a non-zero Collins asymmetry in polarized-proton collisions. The data probe values of Q2 significantly higher than existing measurements from SIDIS. The asymmetries exhibit a dependence on pion z and are consistent in magnitude for the two charged-pion species. For π+, asymmetries are found to be positive; while those for π- are found to be negative. The present data are compared to Collins asymmetry predictions based upon SIDIS and e+e- data. The comparisons are consistent with the expectation for TMD factorization in proton-proton collisions and universality of the Collins fragmentation function. The data show a slight preference for models assuming no suppression from TMD evolution. Further insight into these theoretical questions can be gained from a global analysis, including dihadron asymmetries and Collins asymmetries from STAR.

# Analysis Code

Paper Repository on CVS

# 2012 Lambda D_TT @200GeV

This the webpage contains the supported materials for transverse spin transfer analysis.

Analysis Note:

Support Materials：

• Lambda reconstruction status
• MC production and data comparison
• Trigger Bias
Proceedings:

# Dataset for pp200trans_2012 D_TT analysis

### Statistics Summary

Dataset: pp200trans_2012

Integrated Luminosity: 18.4 pb^-1

Selected Triggers: JP0, JP1, JP2, AJP

#### Event Statistics For Each Trigger:

Trigger JP0 JP1 JP2 AJP Combined
HardXSoft 2.461964e+07 8.525444e+07 1.797188e+07 1.391969e+07 1.417656e+08

# 2) Lambda Reconstruction

The reconstruction of Lambda and anti-Lambda hyperons.

Identification cut on track’s dE/dx measured in TPC is used to find pion and (anti-)proton.

Sketch for Topological Cuts

Values:

a) Statistics of Lambda and anti-Lambda Reconstruction, Inclusive
b) Statistics of Lambda and anti-Lambda Reconstruction, Jet near-side

Only the jet near-side Lambda used to extract D_TT.

The comparison about the reconstruction status with two sets of cut are shown here.
The loose one is the cut set used in run09 D_LL analysis and the tight one.

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

D_TT analysis Record, Rec_Step: all_cut_crp0995

### Invariant Mass

#### Statistics Summary

====> Lambda

JP0

JP1

JP2

AJP

Combined

pt_T [GeV/c]
Central [GeV]
Width [GeV]
N_candidate
bkg fraction
Central [GeV]
Width [GeV]
N_candidate
bkg fraction
Central [GeV]
Width [GeV]
N_candidate
bkg fraction
Central [GeV]
Width [GeV]
N_candidate
bkg fraction
Central [GeV]
Width [GeV]
N_candidate
bkg fraction
1~2 1.1154 0.0016 364262 0.0536 1.1155 0.0016 1394859 0.0570 1.1155 0.0016 280707 0.0666 1.1155 0.0016 338322 0.0546 1.1155 0.0016 2378150 0.0572
2~3 1.1157 0.0021 96583 0.0609 1.1157 0.0021 496180 0.0654 1.1157 0.0022 122363 0.0758 1.1157 0.0021 118569 0.0631 1.1157 0.0021 833695 0.0660
3~4 1.1158 0.0027 25879 0.0612 1.1158 0.0028 186321 0.0649 1.1158 0.0028 57674 0.0748 1.1158 0.0027 38191 0.0609 1.1158 0.0028 308065 0.0659
4~5 1.1161 0.0035 6360 0.0616 1.1160 0.0034 65638 0.0644 1.1159 0.0034 25367 0.0703 1.1160 0.0035 11204 0.0565 1.1160 0.0034 108569 0.0648
5~6 1.1162 0.0041 1782 0.0791 1.1162 0.0042 25415 0.0720 1.1162 0.0042 11736 0.0745 1.1163 0.0042 3484 0.0669 1.1162 0.0042 42417 0.0726
6~8 1.1168 0.0051 729 0.0938 1.1166 0.0051 14932 0.0884 1.1166 0.0052 8622 0.0912 1.1169 0.0051 1853 0.0835 1.1166 0.0051 26136 0.0891

====> anti-Lambda

JP0

JP1

JP2

AJP

Combined

pt_T [GeV/c]
Central [GeV]
Width [GeV]
N_candidate
bkg fraction
Central [GeV]
Width [GeV]
N_candidate
bkg fraction
Central [GeV]
Width [GeV]
N_candidate
bkg fraction
Central [GeV]
Width [GeV]
N_candidate
bkg fraction
Central [GeV]
Width [GeV]
N_candidate
bkg fraction
1~2 1.1155 0.0015 299136 0.0755 1.1155 0.0016 1048977 0.0834 1.1155 0.0015 195241 0.0999 1.1155 0.0015 284645 0.0735 1.1155 0.0017 1827999 0.0823
2~3 1.1157 0.0020 102267 0.0676 1.1157 0.0020 468030 0.0743 1.1157 0.0021 97877 0.0931 1.1157 0.0020 114900 0.0702 1.1157 0.0020 783074 0.0751
3~4 1.1159 0.0026 26961 0.0663 1.1159 0.0026 183094 0.0680 1.1158 0.0027 46292 0.0860 1.1159 0.0027 34646 0.0694 1.1159 0.0027 290993 0.0709
4~5 1.1162 0.0033 5659 0.0660 1.1161 0.0034 61114 0.0635 1.1160 0.0034 19641 0.0741 1.1161 0.0035 8582 0.0678 1.1161 0.0034 94996 0.0662
5~6 1.1166 0.0043 1301 0.0707 1.1163 0.0041 20461 0.0756 1.1162 0.0041 8679 0.0798 1.1164 0.0041 2292 0.0794 1.1163 0.0041 32733 0.0768
6~8 1.1168 0.0057 459 0.1046 1.1168 0.0053 10175 0.1061 1.1166 0.0051 5581 0.1092 1.1172 0.0053 972 0.1086 1.1168 0.0052 17187 0.1072

#### Lambda candidates invariant mass distributions for each p_T range

Trigger: JP0 Distribution Statistics

Trigger: JP1 Distribution Statistics

Trigger: JP2 Distribution Statistics

Trigger: AJP Distribution Statistics

Trigger: Combined Distribution Statistics

#### ant-Lambda candidates invariant mass distributions for each p_T range

Trigger: JP0 Distribution Statistics

Trigger: JP1 Distribution Statistics

Trigger: JP2 Distribution Statistics

Trigger: AJP Distribution Statistics

Trigger: Combined Distribution Statistics

### Distributions for p_T, eta, phi

#### Lambda candidates p_T, eta, phi distributions for each p_T range

Trigger: JP0 pT eta phi

Trigger: JP1 pT eta phi

Trigger: JP2 pT eta phi

Trigger: AJP pT eta phi

Trigger: Combined pT eta phi

#### anti-Lambda candidates p_T, eta, phi distributions for each p_T range

Trigger: JP0 pT eta phi

Trigger: JP1 pT eta phi

Trigger: JP2 pT eta phi

Trigger: AJP pT eta phi

Trigger: Combined pT eta phi

### Distributions of variables used as topolagical cuts

#### Lambda candidates decay length, dca2, dcaV0 and cosrp distributions for each p_T range

Trigger: JP0 decay length dca2 dcaV0 cosrp

Trigger: JP1 decay length dca2 dcaV0 cosrp

Trigger: JP2 decay length dca2 dcaV0 cosrp

Trigger: AJP decay length dca2 dcaV0 cosrp

Trigger: Combined decay length dca2 dcaV0 cosrp

#### anti-Lambda candidates decay length, dca2, dcaV0 and cosrp distributions for each p_T range

Trigger: JP0 decay length dca2 dcaV0 cosrp

Trigger: JP1 decay length dca2 dcaV0 cosrp

Trigger: JP2 decay length dca2 dcaV0 cosrp

Trigger: AJP decay length dca2 dcaV0 cosrp

Trigger: Combined decay length dca2 dcaV0 cosrp

### Distributions of variables of daughter particles

dca of daughters is also used as cut

#### Proton from Lambda candidates: p_T, eta, phi and dca distributions for each p_T range

Trigger: JP0 p_T eta phi dca

Trigger: JP1 p_T eta phi dca

Trigger: JP2 p_T eta phi dca

Trigger: AJP p_T eta phi dca

Trigger: Combined p_T eta phi dca

#### Pion from Lambda candidates: p_T, eta, phi and dca distributions for each p_T range

Trigger: JP0 p_T eta phi dca

Trigger: JP1 p_T eta phi dca

Trigger: JP2 p_T eta phi dca

Trigger: AJP p_T eta phi dca

Trigger: Combined p_T eta phi dca

#### Proton from anti-Lambda candidates: p_T, eta, phi and dca distributions for each p_T range

Trigger: JP0 p_T eta phi dca

Trigger: JP1 p_T eta phi dca

Trigger: JP2 p_T eta phi dca

Trigger: AJP p_T eta phi dca

Trigger: Combined p_T eta phi dca

#### Pion from anti-Lambda candidates: p_T, eta, phi and dca distributions for each p_T range

Trigger: JP0 p_T eta phi dca

Trigger: JP1 p_T eta phi dca

Trigger: JP2 p_T eta phi dca

Trigger: AJP p_T eta phi dca

Trigger: Combined p_T eta phi dca

# 5) Trigger Bias Study

MC samples before and after trigger conditions applying are used for trigger bias study.

• changes in the fractional momentum z of the produced Lambda and anti-Lambda
within the associated jet,
changes in the relative contributions from different hard sub-processes and fragmenting partons with different flavors in the production.
possible differences in the fraction of feed-down contributions.

# 6) Paper proposal

Title:

Transverse spin transfer of Lambda and Anti-Lambda Hyperons in Polarized proton-proton collisonns at \sqrt{s}=200 GeV at RHIC

PAs:  Jincheng Mei，Qinghua Xu

Proposed Target Journal: Phys. Rev. D

Abstract:
The transverse spin transfer from polarized protons to Λ and Λ̄ hyperons is expected to provide sensitivity to the transversity distribution of the nucleon and to the transversely polarized fragmen- tation functions. We report the first measurement of the transverse spin transfer to Λ and Λ̄ along the polarization direction of the fragmenting quark, D_TT, in transversely polarized proton-proton collisions at sqrt{s} = 200 GeV with the STAR detector at RHIC. The data correspond to an integrated luminosity of 18 pband cover the pseudorapidity range |η| < 1.2 and transverse momenta p_up to8 GeV/c. The dependence on p_and η are presented. The D_TT results are found to be comparable with a model prediction, and are also consistent with zero within uncertainties.

Figures:

FIG. 1: The invariant mass distribution for Lambda (open circles) and anti-Lambda (filled circles) candidates for trigger combined sample after selections with 1 <$p_{\mathrm{T}}$ < 8 GeV/c in this analysis.

FIG. 2:
The invariant mass distribution versus cos\theta^{*} for Lambda candidates in the jet near-side with 1< p_T < 8 GeV/c in this analysis as an example.

FIG. 3:

The spin transfer $D _{TT}$ versus cos for a) $\Lambda$ and b) $\bar{\Lambda}$ hyperons, and c) the spin asymmetry $\delta_{TT}$ for the control sample of $K_S^0$ mesons versus cos/theta  in the $p_T$ bin of (2,3) GeV/c for triggered combined sample. The red circles show the results for positive pseudo-rapidity $\eta$ with respect to the polarized beam and the blue squares show the results for negative $\eta$. Only statistical uncertainties are shown.

FIG. 4:
The spin transfer $D_\mathrm{TT}$ for $\Lambda$ and $\bar{\Lambda}$ versus $p_\mathrm{T}$ in polarized proton-proton collisions at $\sqrt{s}=200\,\mathrm{GeV}$ at STAR, in comparison with model predictions for (a) positive $\eta$ and (b) negative $\eta$. The vertical bars and bands indicate the sizes of the statistical and systematic uncertainties, respectively. The $\bar{\Lambda}$ results have been offset to slightly larger $p_T$ values for clarity.

Tables:

TABLE I:  Summary of selection cuts and the Λ and Λ̄ candidate counts and the residual background fractions in each pTbin. Here “DCA” denotes distance of closest approach, and N(σ) quantitatively measures the distance of a particle track to a certain particle band in dE/dx vs. rigidity space[28]. \ver{l} is representative of the vector from PV to Λ decay point and p⃗ is the reconstructed momentum of Λ.

Summary:
In summary, we report the first measurement on the transverse spin transfer, DTT, to Λ and Λ ̄ in transversely polarized proton-proton collisions at \sqrt{s} = 200 GeV at RHIC. The data correspond to an integrated luminosity of 18 pb1 taken at STAR experiment in the year of 2012, which cover mid-rapidity, |η| < 1.2 and pT up to 8GeV/cThe DTT value and precision at the highest pbin, where the effects are expected to be largest, are found to be DTT = 0.031 ± 0.033(stat.) ± 0.008(sys.) for Λ and DTT = 0.034 ± 0.040(stat.) ± 0.009(sys.) for Λ ̄ at η= 0.5 and pT= 6.7 GeV/cThe results for DTTare found to be consistent with zero for Λ and Λ̄ within uncertainties, and are also consistent with model predictions.

Paper draft and review:
Paper draft history
Latest paper draft version:  paperDraft modified with PRD referee

Collaboration review

Analysis Note:

Support Materials：

• Lambda reconstruction status
1, Statistics of Lambda and anti-Lambda Reconstruction, Inclusive
2,
Statistics of Lambda and anti-Lambda Reconstruction, Jet near-side
• MC production and data comparison
1, MC production summary: hard_pT weight
2,
MC and data comparison for inclusive hyperons
3,
MC and data comparison for jet near-side hyperons
• Trigger Bias
Proceedings:
Main Analysis Code:

# A New Users Guide to PDSF Success

Credit goes to Kevin Adkins for the User's guide below:

With the current state of storage on RCF, several users are becoming regular users of PDSF. Anselm and others have requested that I write a short introduction to PDSF. So this blog will hold the keys for successful operation on PDSF. It will expand as issues are broght forth and addressed.

Getting started at PDSF:
1. Get your username at this website: https://nim.nersc.gov/nersc_account_request.php
Once you submit this form you will receive an email that includes a link. This link will only be valid for 72 hours, and will point you to a location where you can set your password. So don't postpone! If you have trouble the email will include a phone number to call, the staff is very helpful so don't hesitate to contact them.
2. Get logged in using the same terminal command as RCF:
3. When entering your password, you only have three chances. After your third chance you'll be "locked out" and you must call to have your password reset. To avoid the hassle, make your password something you can remember!

Storage disks at PDSF:
There are two disks that STAR-spin has access to on PDSF:
/eliza14/star/pwg/starspin/
/eliza17/star/pwg/starspin/
You must email Jeff Porter ( rjporter@bnl.gov ) with your username once you can log in. He will give you access to write on these disks. Once you have the access you can create yourself a folder to write your data to on one or both of the above disks.

Transferring data to PDSF:
PDSF has two Data Transfer Nodes (DTN) that are dedicated to the transfer of data at a high rate. These are
pdsfdtn1.nersc.gov
pdsfdtn2.nersc.gov
Transferring data is best with the rsync command. As an example, assume we have several subdirectories of jets stored in /star/data05/scratch/jkadkins/run12_Jets/ on RCF. To transfer this directory as is to the directory /eliza17/star/pwg/starspin/jkadkins/ on PDSF we would use:
Note that I left off the "/" at the end of the run12_Jets directory above. This means that we will copy all subdirectories to PDSF in the same structure. If the directory at PDSF doesn't exist, it will be created. If there is data already in a directory of the same name on PDSF then the new data will simply be added. If we had left "/" on the end of run12_Jets then we would have copied all files and subdirectories to /eliza17/star/pwg/starspin/jkadkins and not group it into a directory named run12_Jets on PDSF. Give it a test with a few files in a directory to see exactly how this works.
Note: Transferring large volumes of data takes time. To transfer ~90 gigs of data it will take ~60 minutes. So transferring large jet trees or something similar can take a really long time. It may be best to break it up into smaller segments that are more time manageable.

Running code on PDSF:
Code runs EXACTLY the same on PDSF as it does on RCF. PDSF has the same CVS code up to date as on RCF (I'm not sure how often it's updated, but it's all there). So if you use code in CVS on RCF, then you can also use it on PDSF. The only thing that changes is that PDSF doesn't support is the STAR development library. So when running you'll need to use "starpro" or another library.
Submitting jobs is also EXACTLY the same. You'll need an XML (if it works on RCF, it'll work on PDSF without changes) and you'll use the same star-submit command that you use on RCF. The changes come when you want to check the status of your jobs. The two most common commands to manage jobs are:
qstat -u username : Check the status of all jobs you have submitted
qdel -u username : Remove all jobs you currently have submitted
A full list of queue commands can be found here: http://www.nersc.gov/users/computational-systems/pdsf/using-the-sge-batch-system/monitoring-and-managing-jobs/

Finally, problems should be reported to the PDSF hypernews ( pdsf-hn@sun.star.bnl.gov ).

# (A) List of Physics Analysis Projects (obsolete)

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

# Common Analysis Trees

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

### Location

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

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

### Interactive Mode

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

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

 root [6] spinTree-&gt;Draw(&quot;chargedPions.Pt() / ConeJets.Pt()&quot;,&quot;chargedPions.Pt()&gt;0&quot;)

### Batch Mode

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

StRoot/StSpinPool/StSpinTree

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

 //create a new reader StSpinTreeReader *reader = new StSpinTreeReader(); //add some files to analyze, one at a time or in a text file reader-&gt;selectDataset(&quot;$STAR/StRoot/StSpinPool/StSpinTree/datasets/run6_rcf.dataset&quot;); //reader-&gt;selectFile(&quot;./spinAnalyses_6119039.tree.root&quot;);  Ok, so we created a new reader and told it we'd be using the files from Run 6 stored on RCF. You can also give it specfic filenames if you'd prefer, but there's really no reason to do so.  //configure the branches you're interested in (default = true) reader-&gt;connectJets = true; reader-&gt;connectNeutralJets = false; reader-&gt;connectChargedPions = true; reader-&gt;connectBemcPions = true; reader-&gt;connectEemcPions = false; reader-&gt;connectBemcElectrons = false; //optionally filter events by run and trigger //reader-&gt;selectRunList(&quot;$STAR/StRoot/StSpinPool/StSpinTree/filters/run6_jets.runlist&quot;); reader-&gt;selectRun(7143025); //select events that passed hardware OR software trigger for any trigger in list reader-&gt;selectTrigger(137221); reader-&gt;selectTrigger(137222); reader-&gt;selectTrigger(137611); reader-&gt;selectTrigger(137622); reader-&gt;selectTrigger(5); //we can change the OR to AND by doing reader-&gt;requireDidFire = true; reader-&gt;requireShouldFire = true;  In this block we configured the reader to pick up the jets, chargedPions and BEMC pi0s from the files. We also told it that we only wanted to analyze run 7132001, and that we only cared about events triggered by BJP1, L2jet, or L2gamma in the second longitudinal running period.  Finally, we required that one of those trigIds passed both the hardware and the software triggers.

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

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

### What's Included?

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

Run 5
1. skimEvent
2. ConeJets
3. chargedPions
4. bemcPions
Run 6
1. skimEvent
2. ConeJets12
3. ConeJetsEMC
4. chargedPions -- see (Data Collection)
5. bemcPions
6. bemcElectrons

### Known Issues

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

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

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

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

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

### Future Plans

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

# Cuts Summary

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

## Run 5

Event
• standard spinDB requirements
• production triggers only
ConeJets
• 0.2 < detEta < 0.8
• 0.1 < E_neu / E_tot < 0.9
chargedPions
• pt > 2
• -1 < eta < 1
• nFitPoints > 25
• |DCA_global| < 1
• -1 < nSigmaPion < 2
bemcPions
• pt > 3.0
• photon energies > 0.1
• asymmetry < 0.8
• 0.08 < mass < 0.25
• charged track veto
• BBC timebin in {7,8,9}

## Run 6

Event
• standard spinDB requirements
• production triggers + trigId 5 (L2gamma early runs)
ConeJets, ConeJetEMC -- no cuts applied

chargedPions
• pt > 2
• -1 < eta < 1
• nFitPoints > 25
• |DCA_global| < 1
• -1 < nSigmaPion < 2
bemcPions
• pt > 5.2
• photon energies > 0.1
• asymmetry < 0.8
• 0.08 < mass < 0.25
• charged track veto
• BBC timebin in {7,8,9} update:  timebin 6 added in 2007-07-18 production
• both SMD planes good
bemcElectrons added as of 2007-07-18 production
• hardware or software trigger in (117001, 137213, 137221, 5, 137222, 137585, 137611, 137622)
• Global dE/dx cut changing with momentum
• nFitPoints >= 15
• nDedxPoints >= 10
• nHits / nPoss >= 0.52
• track Chi2 < 4
• DCAGlobal < 2
• NEtaStrips > 1 && NPhiStrips > 1
• Primary dE/dx cut changing with momentum
• 0.3 < P/E < 1.5
• -0.01287 < PhiDist < 0.01345
• ZDist in [-5.47,1.796] (West) or [-2.706,5.322] (East)

# Introduction at Spin PWG meeting - 5/10/07

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

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

### Features

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

### Included Analyses

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

### Current Status

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

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

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

# 2006.07.31 First Look at SMD gamma/pi0 Discrimination

Pibero Djawotho

Indiana University
July 31, 2006

## Simulation

Simulation were done by Jason for the SVT review.

## Maximal side residual

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

## xy distribution of SMD hits

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

## 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

macros

# 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

macros

# 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);


# 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=σcore -- width of the narrow Gaussian
• p3=γ -- relative contribution of the wide Gaussian to the area/height
• p4=σtail -- width of the wide Gaussian

## Simulation

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

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

Ditto in log scale.

Ditto by sector.

### Fit Widths

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

## Data from 2005 pp200 EEMC HT 1 and 2 triggers

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

Ditto in log scale.

## Data from 2005 pp200 electrons

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

• Epreshower1 > 5 MeV
• Epreshower2 > 5 MeV
• 0.75 < p/Etower < 1.25
• 3 < dE/dx < 4 keV/cm

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

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

# 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.

## 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.

## Conclusion

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

## References

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

# 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 Etower/Ecluster vs. eta where the cluster consists of 3x3 towers centered around the max energy tower.

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

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

Sampling fraction integrated over all eta's.

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

# Reconstructed/Monte Carlo Muon Energy

10k muons thrown by Will with:

• zvertex=0
• Flat in pT 5-60 GeV/c
• Flat in η 1.1-2

## Etowertanh(eta) vs. eta

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

# 160 GeV photons

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

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

# 20 GeV photons

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

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

# 80 GeV photons

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

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

# Reconstructed/Monte Carlo Electron Energy

## E=2 GeV

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

# 10 GeV photons

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

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

# 40 GeV photons

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

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

# 5 GeV photons

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

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

# Summary of Reconstructed/Monte Carlo Photon Energy

## Description

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

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

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

entire

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

## Conclusions

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

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

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

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

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


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

# gamma/pi0 separation in EEMC using linear cut

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

# gamma/pi0 separation in EEMC using quadratic cut

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

# gamma/pi0 separation in EEMC using quadratic cut

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

# 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.

### E=80-90 GeV

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

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

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

# 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.

### Number of prompt photons per event from GEANT record

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

# Partonic aLL

## Gamma

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

# gamma pT=9-11 GeV

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

# gamma-jet kinematics

Clusters without parent track

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

# gamma/X separation by energy

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

# gamma/X separation by energy with pT weights

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

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

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

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

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

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

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

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

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

# How to run the gamma fitter

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

 root4star StRoot/StGammaMaker/macros/RunGammaFitterDemo.C

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

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

# 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.

## Candidates selection

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

## Raw SMD response

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

Hi Pibero,

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

Steve


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

# 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 pT > 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: pT distribution of tracks before E/p, dE/dx and pT cut

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

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

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

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

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

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

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

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

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

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

Figure 14: dE/dx of tracks before E/p and dE/dx cuts (all pT 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

21 electrons

### Longitudinal

99 electrons

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

# 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.

## Documents

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

# ESMD shape library

## Shower Widths for Monte Carlo and Data

### Description

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

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

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

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


## Decsription of Pythia Sample

A few histograms were added to the code:

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

Figure 1:

Data vs. MC mean u-strip

Figure 2:

Data vs. MC mean v-strip

Figure 3:

Data vs. MC u-strip sigma

Figure 4:

Data vs. MC v-strip sigma

Figure 5:

MC E

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

Hal Spinka
Pibero Djawotho
Last modified Wed Feb 27 09:51:27 EST 2008

# ESMD QA for run 7136033

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

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

## Introduction

In case you missed it, the first look is

here

. I processed

44 runs

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

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

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

Note, the workhorse function is

StEtaFinder::findTowerPoints()

.

## Algorithm

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

# 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.

## E_SMD/E_reco vs. eta

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

# 2008.03.21 Chi square method

Chi square method

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


# 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&amp;&amp;pre2==0) and (pre1&gt;0||pre2&gt;0)

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

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

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

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

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

All fits to the data are with reference to a single

Shower Shape

. The fit function is:

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

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

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

# Data-Driven Residuals

## 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)

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

# 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, |ηjet|<1, and the gamma to the Endcap, 1<ηgamma<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 pT thresholds is listed in the table below. No efficiencies are included.

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

## Gamma-Jets pT slope

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

## References

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

# Jet Finder QA

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

# Run 6 Photon Yield Per Trigger

## Introduction

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

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

The luminosity sampled by each trigger was also caclulated here by Jamie Dunlop. The luminosity for the relevant triggers is reproduced in the table below for convenience. The figure-of-merit (FOM) is calculated as FOM=Luminosity*PB*PY for transverse runs and FOM=Luminosity*PB2*PY2 for longitudinal runs where PB is the polarization of the blue beam and PY is the polarization of the yellow beam. Naturally, in spin physics, the FOM is the better indicator of statistical precisison.

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

## Event selection

### Trigger selection

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

### Gamma candidates

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

StGammaMaker

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

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

.

### Track isolation

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

### EMC isolation

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

Tgamma

/E

Tcone

> 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

jet

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

/eliza13/starprod/jetTrees/2006/trees/

. A local mirror exists at RCF under the directory

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

.

### Spin Information

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

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

### Event Summary

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

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

### 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.

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

# Number of Jets

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

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

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

# Gamma-jets pT distributions

Note:

No cuts on residuals applied.

# References

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

# Binning the shower shape library

## Shower Shapes

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

# Jet A_LL Systematics

## Hypernews discussion

jet A_LL systematic possibility

## References

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

# 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 ALL [1] is demonstrated in this section. For an average number of interactions for each unpolarized bunch crossing, Neff, 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 pT were used. As an example, the prompt photon processes for the partonic pT bin 9-11 GeV and their total cross sections are listed in the table below. Each partonic pT 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 pT bins has been tabulated by Michael Betancourt and is reproduced here for convenience.

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

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

 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9

## Partonic kinematics reconstruction

 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)

 Figure 16 (a) Figure 16 (b): quark from proton 1 (blue beam, +z direction) Figure 16 (c): quark from proton 1 (blue beam, +z direction) and q + g -> q + gamma subprocess (29) Figure 16 (d): quark from proton 1 (blue beam, +z direction) and q + qbar -> gamma + g subprocess (14)

## References

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

# 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

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  Pibero Djawotho Last updated on Sun Jul 20 23:35:39 EDT 2008 # 2008.07.23 Hot Strips Identified by Hal Spinka # Hot Strips Identified by Hal Spinka ## Run 7136034 Sector 8 ### Strips 08U020, 08U080, 08V185 and 08V225 ## Run 7137036 Sector 9 ### Strips 09V064 Pibero Djawotho Last updated Wed Jul 23 03:40:54 EDT 2008 # 2008.07.24 Strips from Weihong's 2006 ppLong 20 runs # Strips from Weihong's 2006 ppLong 20 runs ## Energy [GeV] vs. strip id 2006ppLongRuns.pdf ## Raw ADC vs. strip id Pibero Djawotho Last updated Thu Jul 24 10:35:50 EDT 2008 # G/h Discrimination Algorithm (Willie) My blog pages, from first to last: 01/25: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/jan/25/photon-analysis-progress-week-1-21-08-1-25-08. This post discusses the problem with the spike in secondary tracks at eta=1 in our single-particle simulations. 01/28: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/jan/28/further-qa-plots. This post has QA plots for every particle sample Ross generated, both in the barrel and in the endcap. 02/01: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/01/more-qa-plots-time-efficiencies. This post has QA plots for gamma and piminus (barrel and endcap) as well as reconstruction efficiencies. 02/04: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/04/photon-qa-efficiency-plots-error-bars. This post adds error bars to the reconstruction efficiencies for the photon barrel sample. 02/05: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/05/first-clustering-plots. This post has the first clustering plots, for muons and gammas (barrel only), showing cluster energy, energy-weighted cluster eta and phi, and the number of seeds and clusters passing the thresholds for each event. 02/12: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/12/preshower-plots. This post has preshower plots from the gamma barrel sample, but the plots are of all preshowers in the event and use the preshower information generated by the BEMC simulator and so are not useful. 02/13: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/13/more-clustering-plots. This post has geant QA plots combined with the clustering plots from 02/05 above, but for the gamma and piminus barrel samples. 02/19: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/19/cluster-track-matching-plots. This post investigates the cluster-to-track matching for the gamma barrel sample, using a simple distance variable d=sqrt((delta eta)^2+(delta phi)^2)) to match clusters to tracks and plotting the resulting energy distributions, the energy ratio, etc. 02/21: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/21/more-preshower-plots. This post plots preshower distributions but uses the preshower information from the BEMC simulator and so is not useful. 02/28: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/28/further-preshower-plots-not-completed-yet. Figures 1, 3, and 5 in this post plot the geant preshower energy deposition for gammas, piminuses, and muons (Figs 2, 4, and 6 plot reconstructed preshower information again and so are not useful). 03/04: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/mar/04/muon-preshower-plots. This post expands on the post of 02/28, with additional plots using the geant preshower information, including preshower cluster energy vs. tower cluster energy. 03/06: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/mar/06/first-physics-cuts. This post basically recaps the previous post and adds a cut: unfortunately the cut is based partly on the thrown particle energy and so is not useful. 03/18: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/mar/18/smd-qa-plots. This post plots energy-weighted SMD phi and eta distributions, as well as the total energy deposited in the BSMDE and BSMDP strips located behind a cluster. 03/28: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/mar/28/smd-clustering-plots. This post contains SMD clustering plots for barrel gamma and piminus samples. # Neutral Pions 2005: Frank Simon Information about the 2005 Spin analysis (focused on A_LL and <z>, some QA plots for cross section comparisons) will be archived here. The goal is obviously the 2005 Pi0 spin paper. # Invariant Mass and Width: Data-MC Here I show the invariant masses and corresponding widths I obtain using my cross section binning. These are compared to MC values. The Method: • Invariant mass Data histograms (low mass background and combinatoric background subtracted) are fitted with a gaussian in the range 0.1 - 0.18 GeV/c^2. This gives the mass (gaussian mean) and width (gaussian sigma) • MC invariant mass histograms are obtained from correctly associated MC Pi0s after reconstruction. No weighting of the different partonic pt samples is performed. This can (and will) introduce a bias • Then the same fitting procedure as for data is applied The results are shown in the two figures below. Mass: Width: # Neutral Pion Paper: 2005 ALL & <z> Neutral Pion Paper for 2005 data: Final Results. There are two spin plots planned for the paper, one with the 2005 A_LL and one with the <z>. In addition to this the cross section will be included (analysis by Oleksandr used for publication). ## Final result for A_LL: Figure 1: Double longitudinal spin asymmetry for inclusive Pi0 production. The curves show predictions from NLO pQCD calculations based on the gluon distributions from the GRSV, GS-C and DSSV global analyses. The systematic error shown by the gray band does not include a 9.4% normalization uncertainty due to the polarization measurement. The chi2/ndf for the different model curves are: GRSV Std: 0.740636 GRSV Max: 3.49163 GRSV Min: 0.94873 GRSV Zero: 0.546512 GSC: 0.513751 DSSV: 0.543775 ## Final Result for <z>: Figure 2: Mean momentum fraction of Pi0s in their associated jet as a function of p_T for electromagnetically triggered events. The data points are plotted at the bin center in pion p_T and are not corrected for acceptance or trigger effects. Systematic errors, estimated from a variation of the cuts, are shown by the grey band underneath the data points. The lines are results from simulations with the PYTHIA event generator. The solid line includes detector effects simulated by GEANT, while the dotted line uses jet finding on the PYTHIA particle level. The inset shows the distribution of pT, π / pT, 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 <z> Details The goal of this analysis is to relate the neutral pions to the jets they are embedded in. The analysis is done using the common spin analysis trees, which provide the necessary tools to combine the jet and neutral pion analysis. A neutral pion is associated to a parent jet if it is within the jet cone of 0.4 in eta and phi. To avoid edge effects in the detector, only neutral pions with 0.3 < eta < 0.7 are accepted. ## Cut details: E_neutral / E_total < 0.95 higher energy photon of Pi0 > 2.8 GeV (HT1 trigger); > 3.6 GeV (HT2 trigger) combination HT1/HT2: below 5.7 GeV only HT1 is used, above that both HT1 and HT2 are accepted The final result uses both HT1 and HT2 triggers, but a trigger separated study has also been done, as shown below. There, HT2 includes only those HT2 triggers that do not satisfy HT1 (because of prescale). Figure 1: <z> for Pi0 in jets as a function of p_T for HT1 and HT2 triggers. Also shown is the mean jet p_T as a function of pion p_T. ## Bin-by-Bin momentum ratio Figure 2: Bin-by-bin ratio of pion to jet p_T. The <z> is taken from the mean of these distributions, the error is the error on the mean. A small fraction of all entries have higher Pi0 p_T than jet p_T. Similar behavior is also observed for Pythia MC with GEANT jets. This obviously increases the <z>. An alternative would be to reject those events. The agreement with MC becomes worse if this is done. Here is the data - MC comparison for 3 of the above bins. For the simulation, the reconstruction of the Pi0 is not required to keep statistics reasonable, so the true Pi0 pt is used. However, the MC jet finding uses all momenta after Geant, this is why the edges are "smoother" in the MC plot than in the data plots. Since <z> is an average value, this is not expected to be affected by this, since on average the Pi0 pt is reconstructed right. Figure 3: Data / MC for Bin 5: 6.7 to 8 GeV Figure 4: Data / MC for Bin 6: 8 to 10 GeV Figure 5: Data / MC for Bin 7: 10 to 12 GeV # A_LL Details Details on the A_LL result and the systematic studies: ## The result in numbers:  Bin [GeV] in bin A_LL stat. error syst. error 1 4.17 0.01829 0.03358 0.01603 2 5.41 -0.01913 0.02310 0.01114 3 7.06 0.00915 0.03436 0.01343 4 9.22 -0.06381 0.06366 0.01862 ## A_LL as separated by trigger: Figure 1: A_LL as a function of p_T for HT1 (black) and HT2 (red) triggers separately. HT1 here is taken as all triggers that satisfy the HT1 requirement, but not HT2. Since the HT2 prescale is one, there are very little statistics for HT1 at the highest p_T. The highest p_T point for HT1 is outside the range of the plot, and has a large error bar. The high p_T HT1 data is used in the combined result. ## Systematics: Summary  Bin 1 Bin 2 Bin 3 Bin4 relative luminosity 0.0009 0.0009 0.0009 0.0009 non-longitudinal pol. 0.0003 0.0003 0.0003 0.0003 beam background 0.0012 0.0084 0.0040 0.0093 yield extraction 0.0144 0.0044 0.0102 0.0116 invariant mass background 0.0077 0.0061 0.0080 0.0108 total 0.01603 0.01114 0.01343 0.01862 The first two systematics are common to all spin analyses. The numbers here are taken from the jet analysis. No Pi0 non-longitudinal analysis has been performed due to lacking statistics. These systematics are irrelevant compared to the others. The analysis specific systematics are determined from the data, and as such are limited by statistics. The real systematic limit of a Pi0 analysis with a very large data sit will be much lower. For the yield extraction systematic the invariant mass cuts for the pion yield extraction are varied. The systematic is derived from the maximum change in asymmetry with changing cuts. For the beam background, the systematic is derived by studying how much A_LL changes when the beam background is removed. This is a conservative estimate that covers the scenario that only half of the background is actually removed. The asymmetry of the background events is consistent with zero. For the invariant mass background systematic, A_LL is extracted in three invariant mass bins outside the signal region. The amount of background under the invariant mass peak (includes combinatorics, low mass and others) is estimated from the invariant mass distribution as shown below. For all three bins, the background A_LL is consistent with zero, a "worst case" of value + 1 sigma is assumed as deviation from the signal A_LL. ## Invariant mass distribution: Figure 2: Invariant mass distribution for HT1 events, second p_T bin. The red lines are the MC expectations for Pi0 and Eta, the green line is low mass background, the magenta line is combinatoric background, the thick blue line is a pol2 expectation for the other background, the blue thinner line is the total enveloppe of all contributions, compared to the data. At low mass, the background is overestimated. ## Other systematic studies: False Asymmetries False asymmetries (parity-violating single spin asymmetries) were studied to exclude systematic problems with spin asignments and the like. Of course the absence of problems in the jet analysis with the same data set makes any issues very unlikely, since jet statistics allow much better verifications than Pi0s. Still, single spin asymmetries were studied, and no significant asymmetries were observed. For both triggers, both asymmetries (yellow and blue) and for all p_T bins the asymmetries are consistent with zero, in most cases within one sigma of zero. So there are no indications for systematic effects. The single spin asymmetries are shown below: Figure 3: Single spin asymmetry epsilon_L for the blue beam. Figure 4: Single spin asymmetry epsilon_L for the yellow beam. # Neutral strange particle transverse asymmetries (tpb) ## Neutral strange particle transverse asymmetry analysis Here is information regarding my analysis of transverse asymmetries in neutral strange particles using 2006 p + p TPC data. This follows-on from and expands upon the earlier analysis I did, which can still be found at star.bnl.gov/protected/strange/tpb/analysis/. Comments, questions, things-you'd-like-to-see-done and so forth are welcomed. I'll catalogue updates in my blog as I make them. The links listed below are in 'analysis-order'; best to use these for navigation rather than the alphabetically listed links Drupal links below/in the sidebar. e-mail me at tpb@np.ph.bham.ac.uk # Data used ## Data used in analysis Data used for this analysis is 2006 p+p 200 GeV data taken with transverse polarisation, trigger setup "ppProductionTrans". This spanned days 97 (7th April) to 129 (9th May) inclusive. Trigger bemc-jp0-etot-mb-l2jet (ID 127622) is used. A file catalogue query with the following conditions gives a list of runs for which data is available: trgsetupname=ppProductionTrans,tpc=1,year=2006,sanity=1,collision=pp200,magscale=FullField,filename~physics,library=SL06e,production=P06ie This generates a list of 549 runs. These runs are then compared against the spin PWG run QC (see http://www.star.bnl.gov/protected/spin/sowinski/runQC_2006) and are rejected if any of the following conditions are true: • The run is marked as unusable • The run has a jet patch trigger problem • The run has a spin bits problem • The run is unchecked This excludes 172 runs, leaving 377 runs to be analysed. I use a Maker class to create TTrees of event objects with V0 and spin information for these runs. Code for the Maker and Event classes can be found at /star/u/tpb/StRoot/StTSAEventMaker/ and /star/u/tpb/StRoot/StV0NanoDst/ respectively. Events are accepted only if they fulfill the following criteria: • Event contains specified trigger ID • StSpinDbMaker::isMaskedUsingBx48() returns false • StSpinDbMaker::offsetBX48minusBX7() returns zero TTrees are produced for 358 runs (19 produce no/empty output), yielding 2,743,396 events. The vertex distribution of events from each run are then checked by spin bits. A Kolmogorov test (using ROOT TH1::KolmogorovTest) is used to compare the vertex distributions for (4-bit) spin bits values 5, 6, 9 and 10. If any of the distributions are inconsistent, the run is rejected. Each run's mean event vertex z position is then plotted. Figure 1 shows the distribution, fitted with a Gaussian. A 3σ cut is applied and outlier runs rejected. 38 runs are rejected by these further cuts. The remaining 320 runs, spanning 33 RHIC fills and comprising 2,500,421 events, are used in the analysis.  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 ATT, corrected by the beam polarisation, summed over all pT.  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 K0S 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 Species Pass (millions) Fail (millions) % pass K0S 95.5 48.9 66.2 % Λ 32.5 111.9 22.5 % anti-Λ 11.8 132.5 8.2 % Table 1 # Geometrical cuts ## Geometrical cuts Energy loss cuts are successful in eliminating a significant portion of the background, but further reduction is required to give a clear signal. In addition final yields are calculated by a bin counting method, which requires that the background around the signal peak has a straight line shape. Therefore additional cuts are placed on the V0 candidates based on the geometrical properties of the decay. There are five quantities on which I chose to cut: • Distance of closest approach (DCA) of the V0 candidate to the primary vertex: if the V0 candidate is a genuine particle, its momentum vector should track back to the interaction point. Spurious candidates will not necessarily do so, therefore an upper limit is placed on the approach distance of the V0 to the interaction point. • DCA between the daughter tracks: due to detector resolution the daughter tracks never precisely meet, but placing an upper limit of the minimum distance of approach reduces background from spurious track crossings. • DCAs of the positive and negative daughter tracks to the primary vertex: the daughter tracks are curved due to the magnetic field and a neutral strange particle will decay some distance from the interaction point. Therefore the daughter tracks should not extrapolate back to the primary vertex, but to some distance away from it. Placing a lower limit on this distance can reduce background from tracks originating from the interaction point. • V0 decay distance: neutral strange particles decay weakly, with cτ ~ cm, so the decay vertex should typically be displaced from the interaction point. A lower limit placed on the decay distance of the V0 helps eliminate backgrounds from particles originating at the interaction point. I wrote a class to help perform tuning of these geometrical cut quantities (see /star/u/tpb/StRoot/StV0CutTuning/) by a "brute force" approach; different permutations of the above quantities were attempted, and the resulting mass spectra analysed to see which permutations gave the best balance of background reduction and signal retention. In addition, the consistency of the background to a straight-line shape was required. Due to the limits on statistics, signal retention was considered a greater priority than background reduction. The cut values I decided upon are summarised in table one. Figures one to three show the resulting mass spectra (data are from all runs). Yields are calculated from the integral of bins in the signal (red) region minus the integrals of bins in the background (green) regions. Poisson (√N) errors are used. The background regions are fitted with a straight line, skipping the intervening bins. The signal to background quoted is the ratio of the maximum bin content to the value of the background fit evaluated at that mass. Note that the spectra have the the dE/dx cut included in addition to the geometrical cuts. Species Max DCA V0 to PV* Max DCA between daughters Min DCA + daughter to PV Min DCA − daughter to PV Min V0 decay distance K0S 1.0 1.2** 0.5 0.0** 2.0** Λ 1.5 1.0 0.0** 0.0** 3.0 anti-Λ 2.0** 1.0 0.0** 0.0** 3.0 Table 1: Summary of geometical cuts. All cut values are in centimetres. * primary vertex ** default cut present in micro-DST  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.) Beam polarisation 4-bit 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 fill-by-fill asymmetries for each K0S 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 pT range available. I also divide the data into different transverse momentum bins and calculate the asymmetry as a function of pT. Figures three and four show the pT-dependent asymmetries. No pT 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 NL 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 pT. 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: Species Decay channel Branching ratio K0S π+ + π- 0.692 Λ p + π- 0.639 anti-Λ anti-p + π+ 0.639 Candidate V0s are formed by combining together all possible pairs of opposite charge-sign tracks in an event. The invariant mass of the V0 candidate under different decay hypotheses can then be determined from the track momenta and the daughter masses (e.g. for Λ the positive daughter is assumed to be a proton, the negative daughter a π-minus). Raw invariant mass spectra are shown below. The spectra contain three contributions: real particles of the species of interest; neutral strange particles of a different species; combinatorial background from chance positive/negative track crossings.  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. # placeholder For later use. # Photon-jet with the Endcap (Ilya Selyuzhenkov) ### Gamma-jets ### W-analysis # 2008 ## Year 2008 posts # 01 Jan ## January 2008 posts # 2008.01.30 Selecting gamma-jet candidates out of the jet trees Ilya Selyuzhenkov January 30, 2008 ### Data set jet trees by Murad Sarsour for pp2006 run, runId=7136022 (~60K events, no triggerId cuts yet) ### Jets gross features • Figure 1: Distribution of number of jets per event. Same data on a log scale is here. • Figure 2: Distribution of electromagnetic energy (EM) fraction, R_EM, for di-jet events (number of jets/event = 2). R_EM = [E_t(endcap)+E_t(barrel)]/E_t(total). Black histogram is for R_EM1 = max(Ra, Rb), red is for R_EM2 = min(Ra, Rb). Ra and Rb are EM fraction for jets in the di-jet event. Same data on a log scale is here. ### Gamma-jet isolation cuts list: 1. selecting di-jet events with one of the jet dominated by EM energy, and another one with more hadronic energy: R_EM1 >0.9 and R_EM2 < 0.9 2. selecting di-jet events with jets pointing opposite in azimuth: cos(phi1 - phi2) < -0.9 3. requiring the number of associated charged tracks with a first jet (with maximum EM fraction) to be less than 2: nChargeTracks1 < 2 4. requiring the number of fired EEMC towers associated with a first jet (with maximum EM fraction) to be 1 or 2: 0 < nEEMCtowers1 < 3 ### Applying gamma-jet isolation cuts • Figure 3: Distribution of eta vs number of EEMC towers for the first jet (with maximum EM fraction). Cuts:1-3 applied (no 0 < nEEMCtowers1 < 3 cut). • Figure 4: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction) vs transverse momentum, pt2, of the second jet. Cuts:1-4 applied • Figure 5: Distribution of mean transverse momentum, < pt1 >, of the first jet (with maximum EM fraction) vs transverse momentum, pt2, of the second jet. Cuts:1-4 applied • Figure 6: Distribution of pseudorapidity, eta1, of the first jet (with maximum EM fraction) vs pseudorapidity, eta2, of the second jet. Cuts:1-4 applied • Figure 7: Distribution of azimuthal angle, phi1, of the first jet (with maximum EM fraction) vs azimuthal angle, phi2, of the second jet. Cuts:1-4 applied • Figure 8: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction) vs transverse energy sum for the EEMC towers associated with this jet. Cuts:1-4 applied • Figure 9: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction) vs transverse momentum, pt2, of the second jet. Cuts:1-4 + Et(EEMC) > 3.0 GeV # 02 Feb ## February 2008 posts # 2008.02.13 Gamma-jet candidates: EEMC response Ilya Selyuzhenkov February 13, 2008 ### Data sample Gamma-jet selection cuts are discussed here. There are 278 candidates found for runId=7136022. Transverse momentum distribution for the gamma-jet candidates can be found here. ### Vertex z distribution for di-jet and gamma-jet events • Figure 1: Vertex z distribution. Red line presents gamma-jet candidates (scaled by x50). Black is for all di-jet events. Same data on a log scale is here. • Figure 2: Average vertex z as a function of transverse momentum of the fist jet (with a largest EM energy fraction). Red is for gamma-jet candidates. Black is for all di-jet events. Strong deviation from zero for gamma-jet candidates at pt < 5GeV? ### EEMC response for the gamma-jet candidate EEMC response event by event for all 278 gamma-jet candidate can be found in this pdf file. Each page shows SMD/Tower energy distribution for a given event: 1. First row on each page shows SMD response for the sector which has a maximum energy deposited in the EEMC Tower (u-plane is on the left, v-plane is on the right). 2. In the left plot (u-plane energy distribution) numerical values for pt of the first jet (with maximum EM fraction) and the second jet are given. 3. In addition, fit results assuming gamma (single Gaussian, red line) or neutral pion (double Gaussian, blue line ~ red+green) hypotheses are given. 4. m_{gamma gamma} value (it is shown in the right plot for v-plane). If m_{gamma gamma} value is negative, then the reconstruction procedure has failed (for example, no uv-strips intersection found, or tower energy and uv-strips intersection point mismatch, etc). EEMC response for these "bad" events can be found in this pdf file. If reconstruction procedure succeded, then m_{gamma gamma} gives reconstructed invariant mass assuming that two gammas hit the calorimeter. Figure 3: invariant mass distribution (assuming pi0 hypothesis). Note, that I'm still working on my fitting algorithm (which is not explained here), and fit results and the invariant mass distribution will be updated. 5. It is also shown the ratio for each u/v plane of the integrated single Gaussian fit (red line) to the total energy in the plane (look for "gamma U/V " values on the right v-plane plot). 6. Second and third rows on each page show the energy deposition in the tower, pre-shower1, pre-shower2, and post-shower as a function of eta:phi (etaBin:phiBin). 7. Last row shows the hit distribution in the SMD for all sectors (u-plane on the left, v-plane of the right). ### Playing with a different cuts Trying to isolate the real gammas which hits the calorimeter, I have sorted events into different subsets based on the following set of cuts: 1. EEMC gamma-jet cuts (energetic photon hits EEMC with pt similar or greater to that of the opposite jet) if (invMass < 0) reject if (jet2_pt > jet1_pt) reject if (jet1_pt < 7) reject if (minFraction < 0.75) reject (minFraction = gamma U/V - is a fraction of the integrated single Gaussian peak to the total energy in the uv-plane) Figure 4: Sample gamma-jet candidate EEMC response (all gamma-jet candidates selected according to these conditions can be found in this pdf file): 2. EEMC pi0 cuts: if (invMass < 0) reject if (jet2_pt < jet1_pt) reject if (jet2_pt < 7) reject if (minFraction < 0.75) reject Event by event EEMC response for pi0 (di-jet) candidates selected according to these conditions can be found in this pdf file. # 2008.02.20 Gamma-jet candidates: more statistics from jet-trees Ilya Selyuzhenkov February 20, 2008 ### Short summary After processing all available jet-trees for pp2006 (ppProductionLong), and applying all "gamma-jet" cuts (which are described below): • there are 47K di-jet events selected • for pt1>7GeV there are 5,4K gamma-jet candidates (3,7K with an additional cut of pt1>pt2) • Figure 1: 2,4K events with both pt1, pt2 > 7GeV • 721 candidates within a range of pt1>pt2 and both pt1, pt2 > 7 GeV ### Data set jet trees by Murad Sarsour for pp2006 run, number of runs processed: 323 4.7M di-jet events found (no triggerId cuts yet) ### Di-jets gross features • Figure 2: Distribution of electromagnetic energy (EM) fraction, R_EM, for di-jet events (number of jets/event = 2). R_EM = [E_t(endcap)+E_t(barrel)]/E_t(total). Black histogram is for R_EM1 = max(Ra, Rb), red is for R_EM2 = min(Ra, Rb). Ra and Rb are EM fraction for jets in the di-jet event. Same data on a log scale is here. ### Gamma-jet isolation cuts: 1. selecting di-jet events with one of the jet dominated by EM energy, and another one with more hadronic energy: R_EM1 >0.9 and R_EM2 < 0.9 2. selecting di-jet events with jets pointing opposite in azimuth: cos(phi1 - phi2) < -0.9 3. requiring the number of associated charged tracks with a first jet (with maximum EM fraction) to be less than 2: nChargeTracks1 < 2 4. requiring the number of fired EEMC towers associated with a first jet (with maximum EM fraction) to be 1 or 2: 0 < nEEMCtowers1 < 3 ### Applying gamma-jet isolation cuts • Figure 3: Distribution of eta vs number of EEMC towers for the first jet (with maximum EM fraction). Cuts:1-3 applied (no 0 < nEEMCtowers1 < 3 cut). • Figure 4: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction) vs transverse momentum, pt2, of the second jet. Cuts:1-4 applied • Figure 5: Distribution of mean transverse momentum, < pt1 >, of the first jet (with maximum EM fraction) vs transverse momentum, pt2, of the second jet. Cuts:1-4 applied • Figure 6: Distribution of pseudorapidity, eta1, of the first jet (with maximum EM fraction) vs pseudorapidity, eta2, of the second jet. Cuts:1-4 applied • Figure 7: Distribution of azimuthal angle, phi1, of the first jet (with maximum EM fraction) vs azimuthal angle, phi2, of the second jet. Cuts:1-4 applied • Figure 8: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction) vs transverse energy sum for the EEMC towers associated with this jet. Cuts:1-4 applied • Figure 9: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction) vs transverse momentum, pt2, of the second jet. Cuts:1-4 + Et(EEMC) > 3.0 GeV # 2008.02.27 Tower based clustering algorithm, and EEMC/BEMC candidates Ilya Selyuzhenkov February 27, 2008 ### Gamma-jet candidates before applying clustering algorithm Gamma-jet isolation cuts: 1. selecting di-jet events with the first jet dominated by EM energy, and the second one with a large fraction of hadronic energy: R_EM1 >0.9 and R_EM2 < 0.9 2. selecting di-jet events with jets pointing opposite in azimuth: cos(phi1 - phi2) < -0.8 3. requiring no charge tracks associated with a first jet (jet with a maximum EM fraction): nCharge1 = 0 Figure 1: Transverse momentum Figure 2: Pseudorapidity Figure 3: Azimuthal angle ### Tower based clustering algorithm • for each gamma-jet candidate finding a tower with a maximum energy associated with a jet1 (jet with a maximum EM fraction). • Calculating energy of the cluster by finding all adjacent towers and adding their energy together. • Implementing a cut based on cluster energy fraction, R_cluster, where R_cluster is defined as a ratio of the cluster energy to the total energy in the calorimeter associated with a jet1. Note, that with a cut Ncharge1 =0, energy in the calorimeter is equal to the jet energy. ### Distribution of cluster energy vs number of towers fired in EEMC/BEMC Figure 4: R_cluster vs number of towers fired in EEMC (left) and BEMC (right). No pt cuts. Figure 5: R_cluster vs number of towers fired in EEMC (left) and BEMC (right). Additional cut: pt1>7GeV Figure 6: jet1 pseudorapidity vs number of towers fired in EEMC (left) and BEMC (right). ### R_cluster>0.9 cut: EEMC vs BEMC gamma-jet candidates EEMC candidates: nTowerFiredBEMC=0 BEMC candidates: nTowerFiredEEMC=0 Figure 7: Pseudorapidity (left EEMC, right BEMC candidates) Figure 8: Azimuthal angle (left EEMC, right BEMC candidates) Figure 9: Transverse momentum (left EEMC, right BEMC candidates) ### Number of gamma-jet candidates with an addition pt cuts Figure 10: Transverse momentum (left EEMC, right BEMC candidates): pt1>7GeV Figure 11: Transverse momentum (left EEMC, right BEMC candidates): pt1>7 and pt2>7 # 03 Mar ## March 2008 posts # 2008.03.03 EEMC SMD: u/v-strip energy distribution Ilya Selyuzhenkov March 03, 2008 Data set: ppLongitudinal, runId = 7136033. Some observations/questions: 1. In general distributions look clean and good 2. Sectors 7 and 9 for v-plane and sector 7 for u-plane are noise. 3. Sector 9 has a hot strip (id ~ 120) 4. In sector 3, strips id=0-5 in v-plane are hot (see figure 2 right, bottom) 5. Sectors 2 and 8 in u-plane and sectors 3 and 9 in v-plane have missing strips id=283-288? 6. Strips 288 are always empty? Figure 1:Average energy E in the strip vs sector and strip number (max < E > = 0.0027) same figure on a log scale Figure 2: Average energy E for E>0.02 (max < E > = 0.0682) Same figure on a log scale # 2008.03.12 Gamma-jet candidates: 2-gammas invariant mass and Eemc response Ilya Selyuzhenkov March 12, 2008 ### Gamma-jet candidates: 2-gammas invariant Note: Di-jet transverse momentum distribution for these candidates can be found on figure 11 at this page Figure 1:Invariant mass distribution for gamma-jet candidates assuming pi0 (2-gammas) hypothesys Figure 2:Invariant mass distribution for gamma-jet candidates assuming pi0 (2-gammas) hypothesys with an additional SMD isolation cut: gammaFraction >0.75 GammaFraction is defined as ratio of the integral other SMD strips for the first peak to the total energy in the sector ### EEMC response for the gamma-jet candidates (gammaFraction >0.75) 1. pdf file (first 100 events) with event by event EEMC response for the candidates reconstructed into pion mass (gammaFraction >0.75) 2. pdf file with event by event EEMC response for the candidates not reconstructed into pion mass (second peak not found), but has a first peak with gammaFraction >0.75. # 2008.03.20 Sided residual and chi2 distribution for gamma-jet candidates Ilya Selyuzhenkov March 20, 2008 ### Side residual (no pt cut on gamma jet-candidates) The procedure to discriminate gamma candidate from pions (and other background) based on the SMD response is described at Pibero's web page. Figure 1: Fit integral vs maximum residual for gamma-jet candidates requesting no energy deposited in the EEMC pre-shower 1 and 2 (within a 3x3 clusters around tower with a maximum energy). Black line is defined from MC simulations (see Jason's simulation web page, or Pibero's page above). Figure 2: Fit integral vs maximum residual for gamma-jet candidates requesting requesting no energy deposited in pre-shower 1 cluster and no energy deposited in post-shower cluster (this cut is not really essential in demonstrating the main idea) Figure 3: Fit integral vs maximum residual for gamma-jet candidates requesting requesting non-zero energy deposited in both clusters of pre-shower 1 and 2 ### Side residual: first and second jet pt are greater than 7GeV Event by event EEMC response for gamma-jet candidates for the case of no energy deposited in the EEMC pre-shower 1 and 2 can be found in this pdf file Figure 4: Fit integral vs maximum residual for gamma-jet candidates requesting no energy deposited in the EEMC pre-shower 1 and 2 Figure 5: Fit integral vs maximum residual for gamma-jet candidates requesting requesting no energy deposited in pre-shower 1 cluster and no energy deposited in post-shower cluster Figure 6: Fit integral vs maximum residual for gamma-jet candidates requesting requesting non-zero energy deposited in both clusters of pre-shower 1 and 2 ### Chi2 distribution for gamma-jet candidates ### Monte Carlo shape Event Monte Carlo shape allows to distinguish gammas from background by cutting at chi2/ndf < 0.5 (although the distribution looks wider than for the case of Will's shape). Figure 7: Chi2/ndf for gamma-jet candidates using Monte Carlo shape requesting no energy deposited in both clusters of pre-shower 1 and 2 Figure 8: Chi2/ndf for gamma-jet candidates using Monte Carlo shape requesting non-zero energy deposited in both clusters of pre-shower 1 and 2 ### Will''s shape Less clear where to cut on chi2? Figure 9: Chi2/ndf for gamma-jet candidates using Monte Carlo shape requesting no energy deposited in both clusters of pre-shower 1 and 2 Figure 10: Chi2/ndf for gamma-jet candidates using Monte Carlo shape requesting non-zero energy deposited in both clusters of pre-shower 1 and 2 # 2008.03.26 Sided residual and chi2 distribution for gamma-jet candidates (pre1,2 sorted) Ilya Selyuzhenkov March 26, 2008 ### gamma-jet candidates (no pt cut) Definitions: • F_peak - integral for a fit within [-2,2] strips around SMD u/v peak • D_peak - integral over the data within [-2,2] strips around SMD u/v peak • D_tail^max (D_tail^min) - maximum (minimum) integral over the data tail within +-[3,30] strips from a SMD u/v peak • F_tail is the integral over the fit tail within [3,30] strips from a SMD u/v peak. • Maximum residual = D_tail^max - F_tail All results are for combined distributions from u and v planes: ([u]+[v])/2 Gamma-jet isolation cuts described here Additional quality cuts: 1. Matching between 3x3 tower cluster and u-v high strip intersection 2. At least 4 strips fired within [-2,2] strips from a peak Figure 1: F_peak vs maximum residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2 (within a 3x3 clusters around tower with a maximum energy). Figure 2: F_data vs D_tail^max Note:This plot is fit independend (only the peak position is defined based on the fit) Figure 3: F_data vs D_tail^max-D_tail^max Figure 4: Gamma transverse momentum vs jet transverse momentum ### gamma-jet candidates: pt > 7GeV Figure 5: F_peak vs maximum residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2 (within a 3x3 clusters around tower with a maximum energy). Figure 6: F_data vs D_tail^max Note:This plot is fit independend (only the peak position is defined based on the fit) Figure 7: F_data vs D_tail^max-D_tail^max Figure 8: Gamma transverse momentum vs jet transverse momentum ### gamma-jet candidates: eta, phi, and max[u,v] strip distributions (no pt cuts) Figure 9: Gamma pseudorapidity vs jet pseudorapidity Figure 10: Gamma azimuthal angle vs jet azimuthal angle Note: for the case of Pre1>1 && Pre2==0 there is an enhancement around phi_gamma = 0? Figure 11: maximum strip in v-plane vs maximum strip in u-plane ### Chi2 distribution for gamma-jet candidates (no pt cuts) Figure 12:Chi2/ndf for gamma-jet candidates using Monte Carlo shape (combined for [u+v]/2 plane ) Figure 13:Chi2/ndf for gamma-jet candidates (combined for [u+v]/2 plane ) using Will's shape # 2008.03.28 EEMC SMD shapes: gamma's from gamma-jets (data), MC, and eta-meson analysis Ilya Selyuzhenkov March 28, 2008 ### Some observations: 1. SMD data-driven shapes from different analysis are in a good agreement (Figure 1, upper left plot) 2. Overall MC shape is too narrow compared to the data shapes (Figure 1, upper left plot) 3. Shapes are similar with or without gamma-jet 7GeV pt cut (compare Figures 1 and 2), what may indicate that shape is independent on energy (at least within our kinematic limits). 4. Data-driven and MC shapes are getting close to each other (Figure 4, upper left plot) when requiring no energy above threshold in both preshower layers and with suppressed contribution from pi0 background. The latter is achieved by using the information on reconstructed invariant mass of 2gamma candidates (compare Figure 3 and 4). One interpretation of this can be that in Monte Carlo simulations the contribution from the material in front of the detector is underestimated 5. Energy distribution for each strip in the SMD peak does not looks like a Gaussian (Figure 5), what makes very difficult to interpret results obtained from chi2 analysis (Figure 6-8). 6. Triple Gaussian fit gives a better description of the data shapes, compared to the double Gaussian function (compare red and black lines on Figure 1-4) Figure 1: EEMC SMD shape comparison for various preshower cuts (black points shows u-plane shape only, v-plane results can be found here) Figure 2: EEMC SMD shape comparison for various preshower cuts with gamma-jet pt cut of 7GeV (black points shows u-plane shape only, v-plane results can be found here) Figure 3: Shapes with an additional cut on 2-gamma candidates within pi0 invariant mass range. Sample invariant mass distribution using "simple" pi0 finder can be found here (black points shows u-plane shape only, v-plane results can be found here) Figure 4: Shapes for the candidates when "simple" pi0 finder failed to find a second peak (black points shows u-plane shape only, v-plane results can be found here) Figure 5: Strip by strip SMD energy distribution. Only 12 strips from the right side of the maximum are shown. Zero strip (first upper left plot) corresponds to the high strip in the shape Note, that already at the 3rd strip from a peak, RMS values are comparable to those for a mean, and for a higher strips numbers RMS starts to be bigger that mean. (results for u-plane only, v-plane results can be found here) ### Comparing chi2 distributions for gamma-jet candidates using MC, Will, and Pibero's shapes Results for side residual (together with pt, eta, phi distributions) for gamma-jet candidates can be found at this web page Red histograms on Figures 6-8 shows chi2 distribution from MC-photons (normalized at chi2=1.4) Blue histograms on Figures 6-8 shows chi2 distribution from MC-pions (normalized at chi2=1.4) Figure 6: Chi2/ndf for gamma-jet candidates using Monte Carlo shape Figure 7: Chi2/ndf for gamma-jet candidates using Will's shape (derived from eta candidates based on Weihong's pi0-finder) Figure 8: Chi2/ndf for gamma-jet candidates using Pibero's shape (derived from eta candidates) # 04 Apr ## April 2008 posts # 2008.04.02 EEMC SMD shapes: data-driven (eta, gamma-jet) vs Monte Carlo (single gamma, gamma-jet) Ilya Selyuzhenkov April 02, 2008 ### Some observations: 1. SMD data-driven shapes from eta-meson and gamma-jet studies are in a good agreement for different preshower conditions (compage Fig.1 green circles/triangles in upper-left/bottom-right plots) 2. single gamma MC shapes show preshower dependance, but they are still narrower compared to the data shapes (compare Fig.1 green circles vs blue open squares) 3. MC shapes for gamma-jet and single gamma are consistent (Fig.1, bottom right plot) Figure 1: EEMC SMD shape comparison for various preshower cuts Note:Only MC gamma-jet shape (open red squares) is the same on all plots # 2008.04.02 Sided residual: Using data driven gamma-jet shape (3 gaussian fit) Ilya Selyuzhenkov April 02, 2008 Figure 1: Side residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2 No EEMC SMD based cuts Figure 2: Side residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2 "Simple" pi0 finder can not find a second peak Figure 3: Side residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2 "Simple" pi0 finder reconstruct the invarian mass within [0.1,0.18] range Figure 4: Side residual distribution (Projection for side residual in Figs.1-3 on vertical axis) Figure 5: Signal (green: m < 0) vs background (black, red) separation # 2008.04.02 Sided residual: single gamma Monte-Carlo simulations Ilya Selyuzhenkov April 02, 2008 ### Side residual: single gamma Monte-Carlo simulations Figure 1: Side residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2 No EEMC SMD based cuts # 2008.04.03 chi2-shape subtraction for different Preshower conditions Ilya Selyuzhenkov April 03, 2008 #### Request from Hal Spinka: Hi Ilya, I think you gave up on the chi-squared method too quickly, and am sorry I missed the phone meeting last week. So, I would like to make a request that will hopefully take a minimal amount of your time to show that all is okay. Then, if there is a delay in getting the sided residual information out and into the beam use request, you can still fall back on the chi-squared method. In your March 28 posting, Figure 8 at the bottom, I would like to get numerical values for the events per bin for the black curves. I won't use the preshower1>0 and preshower2=0 data, so those you don't need to send. Also, I won't use the red or blue curve information. I think your problem has been that you normalized your curves at chi-squared/ndf = 1.4 instead of the peak. What I plan to do is to normalize the (pre1=0, pre2=0) to the (pre1=0, pre2>0) data in the peak and subtract. The (pre1=0, pre2=0) set should have some single photons, but also some multiple photons. The (pre1=0, pre2>0) should also have single photons, and more multiple photons, since the chance that one of them will convert is larger. The difference should look roughly like your blue curve, though perhaps not exactly if Pibero's mean shower shape is not perfect (which it isn't). I will do the same thing with taking the difference between (pre1>0, pre2>0) and (pre1=0, pre2=0), and again the difference should look roughly like your blue curve. The (pre1>0, pre2>0) data should have even larger fraction of multiple photons than either of the other two data sets. I would expect the two difference curves to look approximately the same. Hope this is possible for you to do. Since our reduced chi-squared curve looks so much like the one from CDF, I am pretty confident that we are okay, but this should be checked to convince people that we are not doing anything terribly wrong. #### Reply by Ilya: Dear Hal, I have tried to implement your idea and produce a figure attached. There are 4 plots in it: 1. Upper left plot shows normalized to unity (at maximum) chi2 distribution (obtained with Pibero shape for gamma-jet candidates) for a different pre1, pre2 conditions 2. Upper right plot shows bin-by-bin difference: a) between normalized chi2 for pre1=0, pre2>0 and pre1=0, pre2=0 (red) and b) between normalized chi2 for pre1>0, pre2>0 and pre1=0, pre2=0 (blue) 3. Bottom left Same as upper right, but normalization were done based on the integral within [-4,4] bins around maximum. 4. Bottom right Same as for upper right, but with a different normalization ([-4,4] bins around maximum) I have also tried to normalized by the total integral, but the results looks similar. Figure 1: See description above Figure 2: Same without log scale (See description above) # 2008.04.09 Applying gamma-jet reconstruction algorithm for gamma-jet simulated events Ilya Selyuzhenkov April 09, 2008 Data sample: Monte-Carlo gamma-jet sample for partonic pt range of 5-7, 7-9, 9-11,11-15, 15-25, 25-35 GeV. Analysis: Simulated MuDst files were first processed through jet finder algorithm (thanks to Renee Fatemi), and later analyzed by applying gamma-jet isolation cuts (see this link for details) and studying EEMC SMD response (see below). To test the algorithm, Geant records were not used in this analysis. Further studies based on Geant records (yield estimates, etc) are ongoing. #### EESMD shapes comparison Figure 1:Comparison between shower shape profile for data and MC. Black circles shows results for MC gamma-jet sample (all partonic pt). For v-plane results see this figure #### Correlation between gamma and jet pt, eta, phi Figure 2:Gamma vs jet transverse momentum. Figure 3:Gamma vs jet azimuthal angle. Figure 4:Gamma vs jet pseudo-rapidity. #### Results from maximum sided residua study Definitions for F_peak, D_peak, D_tail^max (D_tail^min) can be found here Figure 5:F_peak vs maximum residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2 (within a 3x3 clusters around tower with a maximum energy). Shower shape used to fit data is fixed to the shape from the previous gamma-jet study of real events (see black point on Fig.1 [upper left plot] at this page) Figure 6: F_peak vs D_tail^max: click here Figure 7: F_peak vs D_tail^max-D_tail^min: click here #### Postshower to SMD[uv] energy ratio Figure 8:Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes Figure 8a: Same as figure 8, but for gamma-jet candidates from the real data (no pt cuts). Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes Figure 8b: Comparison between gamma-jet candidates from data with different preshower conditions. Points are normalized in peak to the case of pre1 > 0, pre2 > 0 Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes Figure 8c: Comparison between gamma-jet candidates from Monte-Carlo simulations with different preshower conditions. Points are normalized in peak to the case of pre1 > 0, pre2 > 0 Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes #### Additional QA plots Figure 9: Jet neutral energy fraction Figure 10: High v-strip vs u-strip Figure 11: energy post shower (3x3 cluster) Figure 12: Peak energy SMD-u Figure 13: Peak energy SMD-v Figure 14: Gamma phi Figure 15: Gamma pt Figure 16: Gamma eta Figure 17: Delta gamma-jet pt Figure 18: Delta gamma-jet eta Figure 19: Delta gamma-jet phi #### chi2 distributions Figure 20:chi2 distribution using "standard" MC shape Figure 21:chi2 distribution using Pibero shape # 2008.04.16 Sided residual: Data Driven MC vs raw MC vs 2006 data Ilya Selyuzhenkov April 16, 2008 Figure 1: Sided residual for raw MC (partonic pt 9-11) Figure 2: Sided residual for data-driven MC (partonic pt 9-11) Figure 3: Sided residual for data (pp Longitudinal 2006) #### Different analysis cuts vs number of events which passed the cut 1. N_events : total number of di-jet events found by the jet-finder for gamma in eta region [1,2] (Geant record is used to get this number) 2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi 3. R_{3x3cluster} > 0.9 : Energy in 3x3 cluster of EEMC tower to the total jet energy. 4. R_EM^jet < 0.9 : neutral energy fraction cut for on away side jet 5. N_ch=0 : no charge tracks associated with a gamma candidate 6. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap) 7. N_(5-strip clusler)^u > 3 : minimum number of strips in EEMC SMD u-plane cluster around peak 8. N_(5-strip cluster)^v > 3 : minimum number of strips in EEMC SMD v-plane cluster around peak 9. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc... 10. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluser Figure 4: Number of events which passed various cuts (MC data, partonic pt 9-11) # 2008.04.17 Sided residual: Data Driven MC vs raw MC (partonic pt=5-35) vs 2006 data Ilya Selyuzhenkov April 17, 2008 MC data for different pt weigted according to Michael Betancourt web page: weight = xSection[ptBin] / xSection[max] / nFiles Figure 1: Sided residual for raw MC (partonic pt 5-35) (same plot for partonic pt 9-11) Figure 2: Sided residual for data-driven MC (partonic pt 5-35) (same plot for partonic pt 9-11) Figure 3: Sided residual for data (pp Longitudinal 2006) Figure 4: Sided residual for data (pp Longitudinal 2006) Figure 5: Sided residual for data (pp Longitudinal 2006) Figure 6: pt(gamma) from geant record vs pt(gamma) from energy in 3x3 tower cluster and position for uv-intersection wrt vertex (same on a linear scale) Figure 7: pt(gamma) from geant record vs pt(jet) as found by the jet-finder Figure 8: gamma pt distribution: data-driven MC (red) vs gamma-jet candidates from pp2006 longitudinal run (black). MC distribution normalized to data at maximum for each preshower condition #### Different analysis cuts vs number of events which passed the cut 1. N_events : total number of di-jet events found by the jet-finder for gamma in eta region [1,2] (Geant record is used to get this number) 2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi 3. R_{3x3cluster} > 0.9 : Energy in 3x3 cluster of EEMC tower to the total jet energy. 4. R_EM^jet < 0.9 : neutral energy fraction cut for on away side jet 5. N_ch=0 : no charge tracks associated with a gamma candidate 6. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap) 7. N_(5-strip clusler)^u > 3 : minimum number of strips in EEMC SMD u-plane cluster around peak 8. N_(5-strip cluster)^v > 3 : minimum number of strips in EEMC SMD v-plane cluster around peak 9. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc... 10. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluser Figure 9: Number of events which passed various cuts (MC data, partonic pt 5-35) Red: cuts applied independent Black: cuts applied sequential from left to right # 2008.04.23 Gamma-jet candidates: pp2006 data vs data-driven MC (gamma-jet and bg:jet-jet) Ilya Selyuzhenkov April 23, 2008 ### Sided residual: pp2006 data vs data-driven MC (gamma-jet and bg:jet-jet) MC data for different partonic pt are weigted according to Michael Betancourt web page: weight = xSection[ptBin] / xSection[max] / nFiles Figure 1:Sided residual for data-driven gamma-jet MC events (partonic pt 5-35) Figure 2:Sided residual for data-driven jet-jet MC events (partonic pt 3-55) Figure 3:Sided residual for data (pp Longitudinal 2006) Figure 4:pt(gamma) vs pt(jet) for data-driven gamma-jet MC events (partonic pt 5-35) Figure 5:pt(gamma) vs pt(jet) for data-driven jet-jet MC events (partonic pt 3-55) Figure 6:pt(gamma) vs pt(jet) for data (pp Longitudinal 2006) # 05 May ## May 2008 posts # 2008.05.05 pt-distributions, sided residual (data vs dd-MC g-jet and bg di-jet) Ilya Selyuzhenkov May 05, 2008 Data samples: • pp2006(long) - 2006 pp production longitudinal data after applying gamma-jet aisolation cuts (jet-tree sample: 4.114pb^-1 from Jamie script, 3.164 pb^1 analyses). • gamma-jet - Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV • bg jets - Pythia di-jet sample (~4M events). Partonic pt range 3-65 GeV Figure 1:pt distribution. MC data are scaled to the same luminosity as data (Normalization factor: Luminosity * sigma / N_events). Figure 2:Integrated gamma yield vs pt. For each pt bin yield is defined as the integral from this pt up to the maximum available pt. MC data are scaled to the same luminosity as data. Figure 3:Signal to background ratio (all results divided by the data) ### Sided residual: pp2006 data vs data-driven MC (gamma-jet and bg:jet-jet) You can find sided residual 2-D plots here Figure 4:Maximum sided residual for pt_gamma>7GeV; pt_jet>7GeV Figure 5:Fitted peak for pt_gamma>7GeV; pt_jet>7GeV Figure 6:Max data tail for pt_gamma>7GeV; pt_jet>7GeV Figure 7:Max minus min data tails for pt_gamma>7GeV; pt_jet>7GeV Figure 8:Shower shapes pt_gamma>7GeV; pt_jet>7GeV # 2008.05.08 y:x EEMC position for gamma-jet candidates Ilya Selyuzhenkov May 08, 2008 ### y:x EEMC position for gamma-jet candidates Figure 1:y:x EEMC position for gamma-jet candidates: Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. Figure 2:y:x EEMC position for gamma-jet candidates: Pythia QCD bg sample (~4M events). Partonic pt range 3-65 GeV. Figure 3:y:x EEMC position for gamma-jet candidates: pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger] Figure 3b:y:x EEMC position for gamma-jet candidates: pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger] pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied. ### high u vs. v strip for gamma-jet candidates Figure 4:High v-strip vs high u-strip. Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. Figure 5:High v-strip vs high u-strip: Pythia QCD bg sample (~4M events). Partonic pt range 3-65 GeV. Figure 6:High v-strip vs high u-strip: pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger] Figure 6b:High v-strip vs high u-strip: pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger] pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied. # 2008.05.09 Gamma-jet candidates pt-distributions and TPC tracking Ilya Selyuzhenkov May 09, 2008 ### Detector eta cut study (1< eta < 1.4): • For a three data samples (pp2006 [long], MC gamma-jet, and MC QCD background events) the EEMC detector eta cut of 1< eta < 1.4 has been applied. • Although a poor statistics available for MC background QCD sample, the signal to background ratio (red to green line ratio) getting closer to 1:3 (expected signal to background ratio from Les study). Figure 1:Gamma pt distribution. MC data are scaled to the same luminosity as data (Normalization factor: Luminosity * sigma / N_events). Figure 2:Gamma yield vs pt. MC data are scaled to the same luminosity as data. Figure 3:Signal to background ratio (MC results are normalized to the data) # 2008.05.14 Gamma-cluster to jet energy ratio and away side jet pt matching Ilya Selyuzhenkov May 14, 2008 ### Gamma-cluster to jet1 energy ratio • Correlation between gamma-candidate 3x3 cluster energy ratio (R_cluster) and number of EEMC towers in a jet1 can be found here (Fig. 4). • Gamma pt distribution, yield and signal to background ratio plots for a cut of R_cluster >0.9 can be found here (Figs. 1-3). • Gamma pt distribution, yield and signal to background ratio plots for a cut of R_cluster >0.99 are shown below in Figs. 1-3. One can see that by going from R_cluster>0.9 to R_cluster>0.99 improves signal to background ratio from ~ 1:10 to ~ 1:5 for gamma pt>10 GeV Figure 1:Gamma pt distribution for R_cluster >0.99. MC results scaled to the same luminosity as data (Normalization factor: Luminosity * sigma / N_events). Figure 2:Integrated gamma yield vs pt for R_cluster >0.99 For each pt bin yield is defined as the integral from this pt up to the maximum available pt. MC results scaled to the same luminosity as data. Figure 3:Signal to background ratio for R_cluster >0.99 (all results divided by the data) Compare this figure with that for R_cluster>0.9 (Fig. 3 at this link) ### Gamma and the away side jet pt matching Figure 4: pt asymmetry between gamma and the away side jet (R_cluster >0.9) for a three data samples (pp2006[long] data, gamma-jet MC, QCD jets background). pt cut of 7 GeV for both gamma and jet has been applied. Figure 5: signal to background ratio (R_cluster >0.9) as a function of pt asymmetry between gamma and the away side jet pt cut of 7 GeV for both gamma and jet has been applied. Figure 6: pt asymmetry between gamma and the away side jet (R_cluster >0.99) for a three data samples (pp2006[long] data, gamma-jet MC, QCD jets background). pt cut of 7 GeV for both gamma and jet has been applied. Figure 7: signal to background ratio as a functio of pt asymmetry between gamma and the away side jet (R_cluster >0.99) pt cut of 7 GeV for both gamma and jet has been applied. Figure 8: pt asymmetry between gamma and the away side jet (R_cluster >0.99) for a three data samples (pp2006[long] data, gamma-jet MC, QCD jets background). pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied. Figure 9: signal to background ratio as a function of pt asymmetry between gamma and the away side jet (R_cluster >0.99) pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied. # 2008.05.15 Vertex z distribution for pp2006 data, MC gamma-jet and QCD jets events Ilya Selyuzhenkov May 15, 2008 Figure 1:Vertex z distribution for pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger] Note: In the upper right plot (pre1=0, pre2>0) one can see a hole in the acceptance in the range bweeeen z_vertex -10 to 30 cm (probably due to SVT construction) Figure 1b:Vertex z distribution for pp2006 (same as Fig. 1, but on a linear scale) Figure 2:Vertex z distribution for three different data samples MC results scaled to the same luminosity as data Figure 3:Vertex z distribution for three different data samples pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied. # 2008.05.20 Shower shapes sorted by pre-shower, z-vertex and gamma's eta, phi, pt Ilya Selyuzhenkov May 20, 2008 ### Gamma-jet algorithm and isolation cuts: 1. Selecting only di-jet events identified by the STAR jet finder algorithm, with jets pointing opposite in azimuth: cos(phi_jet1 - phi_jet2) < -0.8 2. Select jet1 with a maximum neutral energy fraction (R_EM1). This is our gamma candidate, for which we further require: • No charge tracks associated with jet1 (default jet radius is 0.7): nChargeTracks_jet1 = 0 Note, that this charge track veto only works in the EEMC region where we do have TPC tracking • No barrel towers associated with jet1 (pure EEMC jet): nBarrelTowers_jet1 = 0 • Ratio of the energy in the 3x3 EEMC high tower cluster to the total jet energy to be: R_cluster>0.99 (previous, softer, cut was 0.9) 3. For the second jet2 (away side jet) we require: • That jet2 has at least ~10% of hadronic energy: R_EM2<0.9 4. Additional gamma candidate QA requirements: • Matching between EEMC SMD uv-strip cluster with a 3x3 cluster of EEMC towers. (in addition reject events for which we can not idetify uv-strip intersection) • Minimum number of strips in 5-strip EEMC SMD uv-plane clusters to be greater that 3. Data sample: • pp2006(long) - 2006 pp production longitudinal data after applying gamma-jet isolation cuts (note the new R_cluster>0.99 cut) ### Shower shapes sorted by pre-shower, z-vertex and gamma's eta, phi, pt Note, that all shapes are normalized at peak to unity Figure 1:Shower shapes for different detector eta bins Figure 2:Shower shapes for different detector phi bins Figure 3:Shower shapes for different gamma pt bins Figure 4:Shower shapes for different z-vertex bins # 2008.05.21 EEMC SMD data-driven library: some eta-meson QA plots Ilya Selyuzhenkov May 21, 2008 ### EEMC SMD data-driven library: some eta-meson QA plots Data sample: • Subset of 441 eta-meson candidates from Will's analysis. • additional QA info (detector eta, pre1, pre2, etc) has been added to pi0-tree reader script: /star/institutions/iucf/wwjacobs/newEtas_fromPi0finder/ReadEtaTree.C • pi0 trees from this RCF directory has been used to regenerate etas NTuple: /star/institutions/iucf/wwjacobs/newEtas_fromPi0finder/out_23/ Some observations: • eta-meson purity within the invariant mass region [0.5, 0.65] is about 72% • Most of the eta-candidates has detector pseudorapidity less or about 1.4, what may limits applicability of data-driven shower shapes derived from these candidates for higher pseudo-rapidity region, where we have most of the background for the gamma-jet analysis due to lack of TPC tracking • z-vertex distribution is very asymmetric, and peaked around -50cm. Only a few candidates has a positive z-vertex values. Figure 1: Eta-meson invariant mass with signal and background fits and ratio (upper left). Pseudorapidity [detector and wrt vertex] distributions (right top and bottom plots), vertex z distributions (bottom left) Figure 2:2D plots for the eta-meson invariant mass vs azimuthal angle (upper left), pseudorapidity (upper right), z-vertex (bottom right), and detector pseudorapidity (bottom right) # 2008.05.27 Shower shapes: pp2006 data, MC gamma-jet and QCD jets, gammas from eta Ilya Selyuzhenkov May 27, 2008 ### Shower shapes and triple Gaussian fits for gammas from eta-meson Figure 1: Shower shapes and triple Gaussian fits for photons from eta-meson sorted by different conditions of EEMC 1st and 2nd pre-shower layers. Note: All shapes have been normalized at peak to unity Triple Gaussian fit parameters: Pre1=0 Pre2=0 0.669864*exp(-0.5*sq((x-0.46016)/0.574864))+0.272997*exp(-0.5*sq((x-0.46016)/-1.84608))+0.0585682*exp(-0.5*sq((x-0.46016)/5.49802)) Pre1=0 Pre2>0 0.0694729*exp(-0.5*sq((x-0.493468)/5.65413))+0.615724*exp(-0.5*sq((x-0.493468)/0.590723))+0.314777*exp(-0.5*sq((x-0.493468)/2.00192)) Pre1>0 Pre2>0 0.0955638*exp(-0.5*sq((x-0.481197)/5.59675))+0.558661*exp(-0.5*sq((x-0.481197)/0.567596))+0.345896*exp(-0.5*sq((x-0.481197)/1.9914)) ### Shower shapes: pp2006, MC gamma-jet and QCD jets, gammas from eta Shower shapes comparison between different data sets: • gammas from eta-meson decay. Obtained from Will's eta-meson analysis • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts. • gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. • QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV. Some observations: • Shapes for gammas from eta-meson decay are in a good agreement with those from MC gamma-jet sample (compare red squares with blue triangle in Fig. 2 and 3). MC gamma-jet shapes obtained by running a full gamma-jet reconstruction algorithm, and this agreement indicates that we are able to reconstruct gamma shapes which we put in with data-driven shower shape library. • MC gamma-jet shapes match pp2006 data shapes for pre1=0 condition, where we expect to be very efficient in background rejection (compare red squares with black circles in upper plots of Fig. 2 and 3). This indicates that we are able to reproduce EEMC SMD of direct photons with data-driven Monte-Carlo. • There is no match between Monte-Carlo QCD background jets and pp2006 data for the case when both pre-shower layer fired (pre1>0 and pre2>0). (compare green triangles with black circes in bottom right plots of Fig.2 and 3). This is the region where we know background dominates our gamma-jet candidates. This shows that we still do not reproduce SMD response for our background events in our data-driven Monte-Carlo simulations (note, that in Monte-Carlo we replace SMD response with real shapes for all background photons the same way we do it for direct gammas). Figure 2: Shower shapes comparison between different data sets. Shapes for gamma-jet candidates obtained with the same gamma-jet reconstruction algorithm for three different data samples (pp2006, gamma-jet and QCD jets MC). pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied. Figure 3:Same as Fig. 2, but with no cuts on gamma and jet pt. All shapes are similar to those in Fig. 2 with an additional pt cuts. Note, that blue triangles are the same as in Fig. 2. # 2008.05.30 Eta, phi, and pt distributions for gamma and jet from MC and pp2006 data Ilya Selyuzhenkov May 30, 2008 Three data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts. • gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. • QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV. Figure 1: Gamma eta distribution. pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied. Figure 2: Gamma pt distribution. pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied. Figure 3: Gamma phi distribution. pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied. Figure 4: Away side jet eta distribution. pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied. Figure 5: Away side jet pt distribution. pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied. Figure 6: Gamma-jet delta pt distribution. pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied. Figure 7: Gamma-jet delta eta distribution. pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied. Figure 8: Gamma-jet delta phi distribution. pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied. # 06 Jun ## June 2008 posts # 2008.06.04 Gamma cluster energy in various EEMC layers: data vs MC Ilya Selyuzhenkov June 04, 2008 ### Gamma cluster energy in various EEMC layers: data vs MC Three data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts. • gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. • QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV. Figure 1: Gamma candidate EEMC pre-shower 1 energy (3x3 cluster). pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied. Figure 2: Gamma candidate EEMC pre-shower 2 energy (3x3 cluster). pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied. Figure 3: Gamma candidate EEMC tower energy (3x3 cluster). pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied. Figure 4: Gamma candidate EEMC post-shower energy (3x3 cluster). pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied. Figure 5: Gamma candidate EEMC SMD u-plane energy [5-strip cluster] (Figure for v-plane) pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied. ### Difference between total and gamma candidate cluster energy for various EEMC layers Figure 6: Total minus gamma candidate (3x3 cluster) energy in EEMC pre-shower 1 layer pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied. Figure 7: Total minus gamma candidate (3x3 cluster) energy in EEMC pre-shower 2 layer pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied. Figure 8: Total minus gamma candidate (3x3 cluster) energy in EEMC tower pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied. Figure 9: Total minus gamma candidate (3x3 cluster) energy in EEMC post-shower layer pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied. Figure 10: Total (sector) energy minus gamma candidate (5-strip cluster) energy in EEMC SMD[u-v] layer pt cuts of 7GeV for the gamma and of 5 GeV for the away side jet have been applied. # 2008.06.09 STAR White paper plots (pt distribution: R_cluster 0.99 and 0.9 cuts) Ilya Selyuzhenkov June 09, 2008 ### Gamma pt distribution: data vs MC (R_cluster 0.99 and 0.9 cuts) Three data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts. • gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. • QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV. Numerical values for different pt-bins from Fig. 1-2 Figure 1: Gamma pt distribution for R_cluster >0.9. No energy in both pre-shower layer (left plot), and No energy in pre-shower1 and non-zero energy in pre-shower2 (right plot) Same figure for R_cluster>0.99 can be found here Figure 2: Gamma pt distribution for R_cluster >0.9. No energy in first EEMC pre-shower1 layer (left plot), and non-zero energy in pre-shower1 (right plot) For more details (yield, ratios, all pre12 four conditions, etc) see figures 1-3 here. Figure 3: Gamma pt distribution for R_cluster >0.99. For more details (yield, ratios, all pre12 four conditions, etc) see figures 1-3 here. # 2008.06.10 Gamma-jet candidate longitudinal double spin asymmetry Ilya Selyuzhenkov June 10, 2008 Note: No background subtraction has been done yet The case of pre-shower1=0 (left plots) roughly has 1:1 signal to background ratio, while pre-shower1>0 (right plots) have 1:10 ratio (See MC to data comparison for details). Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts, plus two additional vertex QA cuts: a) |z_vertex| < 100 and b) 180 < bbcTimeBin < 300 • Polarization fill by fill: blue and yellow • Relative luminosity by polarization fills and runs: relLumi06_070614.txt.gz • Equations used to calculate A_LL from the data: pdf file Figure 1: Gamma-jet candidate A_LL vs gamma pt. Figures for related epsilon_LL and 1/Lum scaled by a factor 10^7 (see pdf/html links above for epsilon_LL and 1/Lum definitions) Figure 2: Gamma-jet candidate A_LL vs x_gluon. Figures for related epsilon_LL and 1/Lum scaled by a factor 10^7 Figure 3: Gamma-jet candidate A_LL vs x_quark. Figures for related epsilon_LL and 1/Lum scaled by a factor 10^7 Figure 4: Gamma-jet candidate A_LL vs away side jet pt. Figures for related epsilon_LL and 1/Lum scaled by a factor 10^7 # 2008.06.18 Photon-jet reconstruction with the EEMC detector (talk at the STAR Collaboration meeting) Ilya Selyuzhenkov June 18, 2008 ## Slides Photon-jet reconstruction with the EEMC detector - Part 1: pdf or odp ## Talk outline (preliminary) 1. Introduction and motivation 2. Data samples (pp2006, MC gJet, MC QCD bg) and gamma-jet reconstruction algorithm: 3. Comparing pp2006 with Monte-Carlo simulations scaled to the same luminosity (EEMC pre-shower sorting): 4. EEMC SMD shower shapes from different data samples (pp2006 and data-driven Monte-Carlo): 5. Sided residual plots: pp2006 vs data-driven Monte-Carlo (gammas from eta meson: 3 gaussian fits) 6. Various cuts study: 7. Some QA plots: 8. A_LL reconstruction technique: 9. Work in progress... To do list: • Understading MC background and pp2006 data shower shapes discrepancy • Implementing sided residual technique with shapes sorted by pre1&2 (eta, sector, etc?) • Tuning analysis cuts • Quantifying signal to background ratio • Background subtraction for A_LL, ... • What else? 10. Talk summary # 07 Jul ## July 2008 posts # 2008.07.07 Pre-shower1 < 5MeV cut study Ilya Selyuzhenkov July 07, 2008 Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts. • gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. • QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV. Figure 1: Correlation between 3x3 cluster energy in pre-shower2 vs. pre-shower1 layers Figure 1a: Distribution of the 3x3 cluster energy in pre-shower1 layer (zoom in for Epre1<0.03 region) (pp2006 data vs. MC gamma-jet and QCD events) Figure 2: Shower shapes after pre-shower1 < 5MeV cut. Shapes are narrower than those without pre1 cut (see Fig. 2) Figure 3: Gamma pt distribution with pre-shower1 < 5MeV cut. Compare with distribution withoud pre-shower1 (Fig. 3) ### Sided residual (before and after pre-shower1 < 5MeV cut) Figure 4: Fitted peak vs. maximum sided residual (no pre-shower1 cuts) Only points for pp2006 data are shown. Figure 5: Fitted peak vs. maximum sided residual (after pre-shower1 < 5MeV cut). Only points for pp2006 data are shown. Note that distribution for pre1>0,pre2>0 case are narrower compared to that in Fig.4 (without pre-shower1 cuts). Figure 6: Distribution of maximum sided residual with pre-shower1 < 5MeV cut. # 2008.07.16 Gamma-gamma invariant mass cut study Ilya Selyuzhenkov July 16, 2008 Three data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts. • gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. • QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV. My simple gamma-gamma finder is trying to find a second peaks (clusters) in each SMD u and v planes, match u and v plane high strip intersections, and calculate the invaraint mass from associated tower energies (3x3 cluster) according to the energy sharing between SMD clusters. Figure 1: Gamma-gamma invariant mass plot. Only pp2006 data are shown: black: no pt cuts, red: gamma pt>7GeV and jet pt>5 GeV. Clear pi0 peak in the [0.1,0.2] invariant mass region. Same data on the log scale ### Gamma pt distributions Figure 2: Gamma pt distribution (no inv mass cuts). Figure 3: Gamma pt distribution (m_invMass<0.11 or no second peak found). This cut improves signal to background ratio. Figure 4: Gamma pt distribution (m_invMass>0.11). Mostly background events. ### Shower shapes Figure 5: Shower shapes (no pre1 and no invMass cuts). Good match between shapes in case of no energy in pre-shower1 layer (pre1=0 case). Figure 6: Shower shapes (pre1<5MeV, no invMass cuts). For pre1&2>0 case shapes getting closer to ech other, but still do not match. Figure 7: Shower shapes (cuts: pre1<5MeV, invMass<0.11 or no second peak found). Note, the surprising agreement between eta-meson shapes (blue) and data (black). ### Gamma-gamma invariant mass plots Figure 8: Invariant mass distribution (MC vs. pp2006 data): no pre1 cut Figure 9: Invariant mass distribution (MC vs. pp2006 data): pre1<5MeV Left side is the same as in Figure 8 Figure 10: Invariant mass distribution (MC vs. pp2006 data): pre1>5MeV Left side plot is empty, since there is no events with [pre1=0 and pre1>5MeV] # 2008.07.22 Photons from eta-meson: library QA Ilya Selyuzhenkov July 22, 2008 ### Shower shapes Figure 1: Shower shapes: no energy cuts, only 12 strips from peak (left u-plane, right v-plane). Figure 1a: Shower shapes: no energy cuts, 150 strips from peak (left u-plane, right v-plane). Figure 2: Shower shapes Energy>8GeV (left u-plane, right v-plane). Figure 3: Shower shapes Energy<=8GeV (left u-plane, right v-plane). ### One dimensional distributions Figure 4: Tower energy. Figure 5: Post-shower energy. Figure 6: Pre-shower1 energy. Figure 7: Pre-shower2 energy. Figure 8: Number of library candidates per sector. ### Correlation plots Figure 9: Transverse momentum vs. energy. Figure 10: Distance from center of the detector vs. energy. Figure 11: x:y position. Figure 12: u- vs. v-plane position. # 2008.07.29 Shower shape comparison with new dd-library bins Ilya Selyuzhenkov July 29, 2008 Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts. • gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. • QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV. Latest data-driven shower shape replacement library: • Four pre-shower bins: pre1,2=0, pre1=0,pre2>0 pre1<4MeV, pre1>=4MeV • plus two energy bins (E<8GeV, E>=8GeV) Figure 1: Shower shapes for u-plane [12 strips] Shower shapes for the library are for the E>8GeV bin. Figure 2: Shower shapes for v-plane [12 strips] Figure 3: Shower shapes for u-plane [expanded to 40 strips] Figure 4: Shower shapes for v-plane [expanded to 40 strips] # 08 Aug ## August 2008 posts # 2008.08.14 Shower shape with bug fixed dd-library Ilya Selyuzhenkov August 14, 2008 Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts. • gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. • QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV. Data-driven maker with bug fixed multi-shape replacement: • Four pre-shower bins: pre1,2=0, pre1=0,pre2>0 pre1<4MeV, pre1>=4MeV • plus two energy bins (E<8GeV, E>=8GeV) Figure 1: Shower shapes for u-plane [12 strips] Shower shapes for the library are for the E>8GeV bin. Open squares and triangles represents raw [without dd-maker] MC gamma-jet and QCD background shower shapes respectively Figure 2: Shower shapes for v-plane [12 strips] Figure 3: Shower shapes for u-plane [expanded to 40 strips] Dashed red and green lines represents raw [without dd-maker] MC gamma-jet and QCD background shower shapes respectively Figure 4: Shower shapes for v-plane [expanded to 40 strips] # 2008.08.19 Shower shape from pp2008 vs pp2006 data Ilya Selyuzhenkov August 19, 2008 Data sets: • pp2006 - STAR 2006 ppProductionLong data (~ 3.164 pb^1) "eemc-http-mb-l2gamma" trigger after applying gamma-jet isolation cuts. • pp2008 - STAR ppProduction2008 (~ 5.9M events) "fmsslow" trigger after applying gamma-jet isolation cuts. [Only ~13 candidates has been selected before pt-cuts] • gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. • QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV. Note: Due to lack of statistics for 2008 data, no pt cuts applied on gamma-jet candidates for both 2006 and 2008 date. Figure 1: Shower shapes for u-plane [pp2006 data: eemc-http-mb-l2gamma trigger] Figure 2: Shower shapes for v-plane [pp2006 data: eemc-http-mb-l2gamma trigger] Figure 3: Shower shapes for u-plane [pp2008 data: fmsslow trigger] Figure 4: Shower shapes for v-plane [pp2008 data: fmsslow trigger] # 2008.08.25 di-jets from pp2008 vs pp2006 data Ilya Selyuzhenkov August 25, 2008 Data sets: • pp2006 - ppProductionLong [triggerId:137213] (day 136 only) • pp2008 - ppProduction2008 [triggerId:220520] (Jan's set of MuDst from day 047) Event selection: • Run jet finder and select only di-jet events [adopt jet-finder script from Murad's analysis] • Define jet1 as the jet with largest neutral energy fraction (NEF), and jet2 - the jet with a smaller NEF • Require no EEMC towers associated with jet1 • Select trigger (see above) and require vertex to be found Figure 1: Vertex z distribution (left: pp2008; right: 2006 data) Figure 2: eta vs. phi distribution for the jet1 (jet with largest NEF) . Figure 3: eta vs. z-vertex distribution for the jet1 (jet with largest NEF) . Figure 4: eta vs. z-vertex distribution for the second jet. Figure 5: Transverse momentum distribution for jet1. Figure 6: Number of barrel towers associated with jet1. Figure 7: Number of charge tracks associated with jet1. # 2008.08.26 Shower shape: more constrains for pre1>4E-3 bin Ilya Selyuzhenkov August 26, 2008 Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts. • gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. • QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV. Data-driven library: • Four pre-shower bins: pre1,2=0, pre1=0,pre2>0 pre1<4MeV, pre1>=4MeV • plus two energy bins (E<8GeV, E>=8GeV) Figure 1: Pre-shower1 energy distribution for Pre1>4 MeV: Eta meson library for E>8GeV bin [left] and data vs. MC results [right]. Figure 2: Shower shapes for v-plane [Pre1<10MeV cut] Figure 3: Shower shapes for u-plane [Pre1<10MeV cut] ## Maximum side residual plots Definitions for side residual plot (F_peak, F_tal, D_tail) can be found here For a moment same 3-gaussian shape is used to fit SMD response for all pre-shower bins. Algo needs to be updated with a new shapes sorted by pre-shower bins. Figure 4: Sided residual plot for pp2006 data only [Pre1<10MeV cut] Figure 5: Sided residual projection on "Fitted Peak" axis [Pre1<10MeV cut] Figure 6: Sided residual projection on "tail difference" axis [Pre1<10MeV cut] # 2008.08.27 Gamma-jet candidates detector position for different pre-shower conditions Ilya Selyuzhenkov August 27, 2008 Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts. • gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. • QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV. Figure 1: High u vs. v strip id distribution for different pre-shower conditions. Left column: QCD jets, middle column: gamma-jet, right columnt: pp2006 data Figure 2: x vs. y position of the gamma-candidate within EEMC detector for different pre-shower conditions. Left column: QCD jets, middle column: gamma-jet, right columnt: pp2006 data Figure 3:Reconstructed vs. generated (from geant record) gamma pt for the MC gamma-jet sample. Pre-shower1<10MeV cut applied. Figure 4:Reconstructed vs. generated (from geant record) gamma eta for the MC gamma-jet sample. Pre-shower1<10MeV cut applied. Figure 5:Reconstructed vs. generated (from geant record) gamma phi for the MC gamma-jet sample. Pre-shower1<10MeV cut applied. # 09 Sep ## September 2008 posts # 2008.09.02 Shower shape fits Ilya Selyuzhenkov September 02, 2008 Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts. • gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. • QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV. Shower shape fitting procedure: 1. Fit with single Gaussian shape using 3 highest strips 2. Fit with double Gaussian using 5 strips from each side of the peak [11 strips total] First Gaussian parameters are fixed from the step above 3. Re-fit with double Gaussian with initial parameters from step 2 above 4. Fit with triple Gaussian [fit range varies from 9 to 15 strips, default is 12 strips, see below] Initial parameters for the first two Gaussian are fixed from step 3 above 5. Fit with triple Gaussian with initial parameters from step 4 above (releasing all parameters except mean values) Fitting function "[0]*(exp ( -0.5*((x-[1])/[2])**2 )+[3]*exp ( -0.5*((x-[4])/[5])**2 )+[6]*exp ( -0.5*((x-[7])/[8])**2 ))" ## Fit results for MC gamma-jet data sample Figure 1: MC gamma-jet shower shapes and fits for u-plane Results from single, double and triple Gaussian fits (using from 9 to 15 strips) are shown. Figure 2: Same as figure 1. but from v-plane Figure 3: MC gamma-jet results using triple Gaussian fits within 12 strips from a peak. Left: u-plane. Right: v-plane Figure 4: Combined fit results from MC gamma-jet sample Figure 5: Fitting parameters [see equation for the fit function above]. Note, that parameters 1, 4, and 7 (peak position) has the same value. Numerical fit results: 1. pre1=0 pre2=0 [u]: 0.602039*((exp(-0.5*sq((x-0.491324)/0.605927))+(0.578161*exp(-0.5*sq((x-0.491324)/2.05454))))+(0.0937517*exp(-0.5*sq((x-0.491324)/6.37656)))) 2. pre1=0 pre2=0 [v]: 0.729744*((exp(-0.5*sq((x-0.480945)/0.621631))+(0.327792*exp(-0.5*sq((x-0.480945)/2.01717))))+(0.0410935*exp(-0.5*sq((x-0.480945)/6.49599)))) 3. pre1=0 pre2>0 [u]: 0.725212*((exp(-0.5*sq((x-0.474451)/0.560416))+(0.3332*exp(-0.5*sq((x-0.474451)/1.91957))))+(0.0611053*exp(-0.5*sq((x-0.474451)/5.34357)))) 4. pre1=0 pre2>0 [v]: 0.686446*((exp(-0.5*sq((x-0.536662)/0.650485))+(0.388429*exp(-0.5*sq((x-0.536662)/1.99118))))+(0.0712328*exp(-0.5*sq((x-0.536662)/5.64637)))) 5. 0 <4MeV [u]: 0.612486*((exp(-0.5*sq((x-0.485717)/0.592415))+(0.55846*exp(-0.5*sq((x-0.485717)/1.87214))))+(0.0749598*exp(-0.5*sq((x-0.485717)/6.12462)))) 6. 0 <4MeV [v]: 0.651584*((exp(-0.5*sq((x-0.486876)/0.652023))+(0.450767*exp(-0.5*sq((x-0.486876)/2.07667))))+(0.0864232*exp(-0.5*sq((x-0.486876)/5.84357)))) 7. 4 <10MeV [u]: 0.621905*((exp(-0.5*sq((x-0.496841)/0.632917))+(0.512575*exp(-0.5*sq((x-0.496841)/1.97482))))+(0.0927374*exp(-0.5*sq((x-0.496841)/6.10844)))) 8. 4 <10MeV [v]: 0.634943*((exp(-0.5*sq((x-0.505378)/0.660763))+(0.480929*exp(-0.5*sq((x-0.505378)/2.17312))))+(0.0788037*exp(-0.5*sq((x-0.505378)/6.21667)))) ## Fit results for pp2006 gamma-jet candidates Figure 6: Same as Fig. 3, but for gamma-jet candidates from pp2006 data Figure 7: Same as Fig. 5, but for gamma-jet candidates from pp2006 data # 2008.09.09 Maximum sided residual with shower shapes sorted by uv- and pre-shower bins Ilya Selyuzhenkov September 09, 2008 Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts. • gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. • QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV. Procedure to calculate maximum sided residual: 1. For each event fit SMD u and v energy distributions with triple Gaussian functions from shower shapes analysis: [0]*(exp(-0.5*((x-[1])/[2])**2)+[3]*exp(-0.5*((x-[1])/[4])**2)+[6]*exp(-0.5*((x-[1])/[5])**2)) Fit parameters sorted by various pre-shower conditions and u and v-planes can be found here There are only two free parameters in a final fit: overall amplitude [0] and mean value [1] Fit range is +-2 strips from the high strip (5 strips total). 2. Integrate energy from a fit within +-2 strips from high strip. This is our peak energy from fit, F_peak. 3. Calculate tail energies on left and right sides from the peak for both data, D_tail, and fit, F_tail. Tails are integrated up to 30 strips excluding 5 highest strips. Determine maximum difference between D_tail and F_tail: max(D_tail-F_tail). This is our maximum sided residual. 4. Plot F_peak vs. max(D_tail-F_tail). This is sided residual plot. 5. (implementation for this item is in progress) Based on MC gamma-jet sided residual plot find a line (some polynomial function) which will serve as a cut to separate signal and background. Use that cut line to calculate signal to background ratio and apply it for the real data analysis. Figure 1: Maximum sided residual plots for different data sets and various pre-shower condition. Columns [data sets]: 1. MC QCD background; 2. gamma-jet; 3. pp2006 data Rows [pre-shower bins]: 1. pre1=0 pre2=0; 2. pre1=0, pre2>0; 3. 0<pre1<4MeV; 4. 4<pre1<10MeV Results from u and v plane are combined as [U+V]/2 Figure 2: max(D_tail-F_tail) distribution (projection on horizontal axis from Fig.1) Some observations: Results for pp2006 and MC gamma-jet are consistent for pre1=0 pre2=0 case (upper left plot) Results for pp2006 and MC QCD background jets are also in agrees for pre1>0 case (lower left and right plots) Figure 3: F_peak distribution (projection on vertical axis from Fig.1) # 2008.09.16 QA plots for maximum sided residual (obsolete) Ilya Selyuzhenkov September 16, 2008 These results are obsolete. Please use this link instead Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts. • gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. • QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV. Notations used in the plots: • Fit peak energy: F_peak - integral within +-2 strips from maximum strip Maximum strip determined by fitting procedure. Float value converted ("cutted") to integer value. • Data peak energy: D_peak - energy sum within +-2 strips from maximum strip (the same strip Id as for F_peak). • Data tails: D_tail^left and D_tail^right. Energy sum from 3rd strip up to 30 strips on the left and right sides from maximum strip (excludes strips which contributes to D_peak) • Fit tails: F_tail^left and F_tail^right. Same definition as for D_tail, but integrals are calculated from a fit function. • Maximum sided residual: max(D_tail-F_tail) Maximum of the data minus fit energy on the left and right sides from the peak. Figure 1: D_peak from [U+V]/2. Figure 2: U/V asymmetry for D_peak: [U-V]/[U+V] Figure 3: F_peak from [U+V]/2. Figure 4: U/V asymmetry for F_peak: [U-V]/[U+V] Figure 5: (D_peak - F_peak)/D_peak asymmetry Figure 6: Maximum sided residual from V vs. U plane. Figure 7: (D_tail-F_tail)^right vs. (D_tail-F_tail)^left # 2008.09.23 QA plots for maximum sided residual (bug fixed update) Ilya Selyuzhenkov September 23, 2008 Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts (note: R_cluster > 0.9 is used below). • gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. • QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV. Notations used in the plots: • Fit peak energy: F_peak - integral within +-2 strips from maximum strip Maximum strip determined by fitting procedure. Float value converted ("cutted") to integer value. • Data peak energy: D_peak - energy sum within +-2 strips from maximum strip (the same strip Id as for F_peak). • Data tails: D_tail^left and D_tail^right. Energy sum from 3rd strip up to 30 strips on the left and right sides from maximum strip (excludes strips which contributes to D_peak) • Fit tails: F_tail^left and F_tail^right. Same definition as for D_tail, but integrals are calculated from a fit function. • Maximum sided residual: max(D_tail-F_tail) Maximum of the data minus fit energy on the left and right sides from the peak. Figure 1: D_peak from [U+V]/2. Figure 2: (D_peak - F_peak)/D_peak asymmetry Figure 3: Maximum sided residual from V vs. U plane. Figure 4: (D_tail-F_tail)^right. (D_tail-F_tail)^left # 2008.09.23 Right-left SMD tail asymmetries Ilya Selyuzhenkov September 23, 2008 Figure 1: D_peak vs. [right-left] D_tail Figure 2: [right-left]/[right-+left] D_tail # 2008.09.23 Sided residual plot projection: toward s/b efficency/rejection plot Ilya Selyuzhenkov September 23, 2008 Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts (note: R_cluster > 0.9 is used below). • gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. • QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV. Notations used in the plots: • Fit peak energy: F_peak - integral within +-2 strips from maximum strip Maximum strip determined by fitting procedure. Float value converted ("cutted") to integer value. • Data peak energy: D_peak - energy sum within +-2 strips from maximum strip (the same strip Id as for F_peak). • Data tails: D_tail^left and D_tail^right. Energy sum from 3rd strip up to 30 strips on the left and right sides from maximum strip (excludes strips which contributes to D_peak) • Fit tails: F_tail^left and F_tail^right. Same definition as for D_tail, but integrals are calculated from a fit function. • Maximum sided residual: max(D_tail-F_tail) Maximum of the data minus fit energy on the left and right sides from the peak. ## Maximum sided residual: MC vs. data comparison Figure 1: Maximum sided residual plot Top get more statistics for MC QCD sample plot is redone with a softer R_cluster > 0.9 cut Figure 2: D_peak (projection on vertical axis for Fig. 1) Upper left plot (no pre-shower fired case) reveals some difference between MC gamma-jet and pp2006 data at lower D_peak values. This difference could be due to background contribution at low energies. Still needs more statistics for MC QCD jet sample to confirm that statement. Figure 3: max(D_tail-F_tail) (projection on horisontal axis for Fig. 1) One can get an idea of signal/background separation (red vs. black) depending on pre-shower condition. Figure 4: Mean < max(D_tail-F_tail) > vs. D_peak (profile on vertical axis from Fig. 1) For gamma-jet sample average sided residual is independent on D_peak energy and has a slight positive shift for all pre-shower>0 conditions. For large D_peak values (D_peak>0.16) MC gamma-jet and pp2006 data results are getting close to each other. This corresponds to higher energy gammas, where we have a better signal/background ratio, and thus more real gammas among gamma-jet candidates from pp2006 data. (Note: legend's color coding is wrong, colors scheme is the same as in Fig. 3) Figure 5: Mean < D_peak > vs. max(D_tail-F_tail) (profile on horisontal axis from Fig. 1) For "no-preshower fired" case MC gamma-jet sample has a large average values than that from pp2006 data. This reflects the same difference between pp2006 and MC gamma-jet sample at small D_peak values (see Fig. 2, upper left plot). (Note: legend's color coding is wrong, colors scheme is the same as in Fig. 3) Figure 6: D_peak vs. gamma pt Figure 7: D_peak vs. gamma 3x3 tower cluster energy Figure 8: 3x3 cluster tower energy distribution Figure 9: Gamma pt distribution ## Signal/background separation The simplest way to get signal/background separation is to draw a straight line on sided residual plot (Fig. 1) in such a way that it will contains most of the counts (signal) on the left side, and use a distance to that line for both MC and pp2006 data samples as a discriminant for signal/background separation. To get the distance to the straight line one can rotate sided residual plot by the angle which corresponds to the slope of this line, and then project it on "rotated" max(D_tail-F_tail) axis. Figure 10: Shows "rotated" sided residual plot by "5/6*(pi/2)" angle (this angle has been picked by eye). One can see that now most of the counts for gamma-jet sample (middle column) are on the left side from vertical axis. Figure 11: "Rotated" max(D_tail-F_tail) [projection on horizontal axis for Fig. 10] Cut on "Rotated" max(D_tail-F_tail) can be used for signal/background separation. From figure below one can see much better signal/background separation than in Fig. 3 Figure 12: "Rotated" D_peak [projection on vertical axis for Fig. 10] ## Optimizing the shape of s/bg separation line Ideally, instead of straight line one needs to use an actual shape of side residual distribution for MC gamma-jet sample. This shape can be extracted and parametrized by the following procedure: 1. Get slices from sided residual plot for different D_peak values 2. From each slice get max(D_tail-F_tail) value for which most of the counts appears on its left side (for example 80%), 3. Fit these set of points {D_peak slice, max(D_tail-F_tail)} with a polynomial function The distance to that polynomial function can be used to determine our signal/background rejection efficiency. This work is in progress... Just last one figure showing shapes for 6 slices from sided plot. Figure 13: max(D_tail-F_tail) for different slices in D_peak (scaled by the integral for each slice) # 2008.09.30 Sided residual: purity, efficiency, and background rejection Ilya Selyuzhenkov September 30, 2008 Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts (note: R_cluster > 0.9 is used below). • gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. • QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV. Notations used in the plots: • Fit peak energy: F_peak - integral within +-2 strips from maximum strip Maximum strip determined by fitting procedure. Float value converted ("cutted") to integer value. • Data peak energy: D_peak - energy sum within +-2 strips from maximum strip (the same strip Id as for F_peak). • Data tails: D_tail^left and D_tail^right. Energy sum from 3rd strip up to 30 strips on the left and right sides from maximum strip (excludes strips which contributes to D_peak) • Fit tails: F_tail^left and F_tail^right. Same definition as for D_tail, but integrals are calculated from a fit function. • Maximum sided residual: max(D_tail-F_tail) Maximum of the data minus fit energy on the left and right sides from the peak. ## Determining cut line based on sided residual plot Figure 1: Sided residual plot: D_peak vs. max(D_tail-F_tail) Red lines show 4th order polynomial functions, a*x^4, which have 80% of MC gamma-jet counts on the left side. These lines are obtained independently for each of pre-shower condition based on fit procedure shown in Fig. 3 below. Figure 2: max(D_tail-F_tail) distribution (projection on horizontal axis from sided residual plot, see Fig. 1 above) Figure 3: max(D_tail-F_tail) [at 80%] vs. D_peak. For each slice (bin) in D_peak variable, the max(D_tail-F_tail) value which has 80% of gamma-jet candidates on the left side are plotted. Lines represent fits to MC gamma-jet points (shown in red) using different fit functions (linear, 2nd, 4th order polynomials: see legend for color coding). Note, that in this plot D_peak values are shown on horizontal axis. Consequently, to get 2nd order polynomial fit on sided residual plot (Fig. 1), one needs to use sqrt(D_peak) function. The same apply to 4th order polynomial function. Figure 4: D_peak vs. horisontal distance from 4th order polinomial function to max(D_tail-F_tail) values. (compare with Fig. 1: Now 80% of MC gamma-jet counts are on the left side from vertical axis) Figure 5: Horizontal distance from 4th order polynomial function to max(D_tail-F_tail) [Projection on horizontal axis from Fig. 4] Based on this plot one can obtain purity, efficiency, and rejection plots (see Fig. 6 below) ## Gamma-jet purity, efficiency, and QCD background rejection Horizontal distance plotted in Fig. 5 can be used as a cut separating gamma-jet signal and QCD-jets background, and for each value of this distance one can define gamma-jet purity, efficiency, and QCD-background rejection: • gamma-jet purity is defined as the ratio of the integral on the left for MC gamma-jet data sample, N[g-jet]_left, to the sum of the integrals on the left for MC gamma-jet and QCD jets, N[QCD]_left, data samples: Purity[gamma-jet] = N[g-jet]_left/(N[g-jet]_left+N[QCD]_left) • gamma-jet efficiency is defined as the ratio of the integral on the left side for MC gamma-jet data sample, N[g-jet]_left, to the total integral for MC gamma-jet data sample, N[g-jet]: Efficiency[gamma-jet] = N[g-jet]_left/N[g-jet] • QCD background rejection is defined as the ratio of the integral on the right side for MC QCD jets data sample, N[QCD]_right, to the total integral for MC QCD jets data sample, N[QCD]: Rejection[QCD] = N[QCD]_right/N[QCD] Figure 6: Shows: purity[g-jet] vs. efficiency[g-jet] (upper left); rejection[QCD] vs. efficiency[g-jet] (upper right); purity[g-jet] vs. rejection[QCD] (lower left); pp2006 to MC ratio, N[pp2006]/(N[g-jet]+N[QCD]), vs. horizontal distance from Fig. 5 (lower right) # 10 Oct ## October 2008 posts # 2008.10.13 Jet trees for Michael's gamma filtered events Ilya Selyuzhenkov October 13, 2008 I have finished production of jet trees for Michael's gamma filtered events You can find jet and skim file lists in my directory at IUCF disk (RCF): • Jet trees: /star/institutions/iucf/IlyaSelyuzhenkov/simu/JetTrees/JetTrees.list • Skim trees: /star/institutions/iucf/IlyaSelyuzhenkov/simu/JetTrees/SkimTrees.list • Log files: /star/institutions/iucf/IlyaSelyuzhenkov/simu/JetTrees/LogFiles.list Number of jet events is 1284581 (1020 files). Production size, including archived log files, is 4.0G. The script to run jet finder: /star/institutions/iucf/IlyaSelyuzhenkov/simu/JetTrees/20081008_gJet/StRoot/macros/RunJetSimuSkimFinder.C JetFinder and JetMaker code: /star/institutions/iucf/IlyaSelyuzhenkov/simu/JetTrees/20081008_gJet/StRoot/StJetFinder /star/institutions/iucf/IlyaSelyuzhenkov/simu/JetTrees/20081008_gJet/StRoot/StJetMaker For more details see these threads of discussions: # 2008.10.14 Purity, efficiency, and background rejection: R_cluster > 0.98 Ilya Selyuzhenkov October 14, 2008 Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts (note: R_cluster > 0.98 is used below). • gamma-jet - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. • QCD jets - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV. Figure 1: Horizontal distance from 4th order polynomial function to max(D_tail-F_tail) See this page for definition and more details Figure 2: purity[g-jet] vs. efficiency[g-jet] (upper left); rejection[QCD] vs. efficiency[g-jet] (upper right); purity[g-jet] vs. rejection[QCD] (lower left); pp2006 to MC ratio, N[pp2006]/(N[g-jet]+N[QCD]), vs. horizontal distance (lower right) # 2008.10.15 Comparison of gamma-jets from Michael's filtered events vs. old MC samples Ilya Selyuzhenkov October 15, 2008 Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) after applying gamma-jet isolation cuts (note: R_cluster > 0.9 is used below). • gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events. Partonic pt range 2-25 GeV. • QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events. Partonic pt range 2-25 GeV. • gamma-jet [old] - data-driven Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV. Details on jet trees production for Michael's gamma filtered events can be found here. • QCD jets [old] - data-driven Pythia QCD jets sample (~4M events). Partonic pt range 3-65 GeV. Some observations: • Both Fig. 1a vs. Fig. 1b shows good statistics for old and new (gamma-filtered) MC gamma-jet samples • Fig. 1c shows poor statistics for QCD background sample within partonic pt range 5-10GeV (only 3 counts for "pre1=0 & pre2=0" condition). Fig. 1d (new QCD sample) has much more counts in the same region, but it is still only 20-25 entries for the case when none of EEMC pre-shower layers fired (upper left corner - our purest gamma-jet sample). This may be still insufficient for a various cuts systematic study. • Fig. 2 and Fig. 3 shows nice agreement between data and MC for both old and new (gamma-filtered) MC samples. For pre-shower1>0 case this agreement persists across full range of gamma's pt (7GeV and above). Upper plots in Fig. 3 shows some difference between data and Monte-Carlo, what could be effect from l2gamma trigger, which has not been yet applied for MC events. Figure 1a: partonic pt for gamma-jet [old] events after analysis cuts and partonic pt bin weighting (Note:Arbitrary absolute scale) Figure 1b: partonic pt for gamma-jet [gamma-filtered] events after analysis cuts. Michael's StBetaWeightCalculator has been used to caclulate partonic pt weights Figure 1c: partonic pt for QCD jets [old] events after analysis cuts and partonic pt bin weighting (Note:Arbitrary absolute scale) Figure 1d: partonic pt for QCD jets [gamma-filtered] events after analysis cuts. Michael's StBetaWeightCalculator has been used to caclulate partonic pt weights Figure 2: reconstructed gamma pt: old MC vs. pp2006 data (scaled to the same luminosity) Figure 3: reconstructed gamma pt: gamma-filtered MC vs. pp2006 data (scaled to the same luminosity) # 2008.10.15 Purity vs. efficiency from gamma-filtered events: R_cluster > 0.9 vs. R_cluster > 0.98 Ilya Selyuzhenkov October 15, 2008 Data sets: ## Gamma-jet candidates from MC gamma filtered events: R_cluster > 0.9 Figure 1: Horizontal distance from sided residual plot: R_cluster > 0.9 (see Figs. 1-5 from this post for horizontal distance definition) Figure 2: Purity/efficiency/rejection, and data to MC[gamma-jet+QCD] ratio plots: R_cluster > 0.9 (see text above Fig. 6 from this post for purity, efficiency, and background rejection definition) ## Gamma-jet candidates from MC gamma filtered events: R_cluster > 0.98 Figure 3: Reconstructed gamma pt: R_cluster > 0.98 Figure 4: Horizontal distance from sided residual plot: R_cluster > 0.98 Figure 5: Purity/efficiency/rejection, and data to MC[gamma-jet+QCD] ratio plots: R_cluster > 0.98 # 2008.10.21 Shower shapes, 5/25 strips cluster energy, raw vs. data-driven MC Ilya Selyuzhenkov October 21, 2008 Data sets: Some comments: • Overall comment: effect of data-driven shower shape replacement procedure on QCD background events is small, except probably pre1=0 pre2=0 case. • Fig. 1-3, upper left plots (pre1=0 pre2=0) show that average energy per strip in data-driven gamma-jet MC (i.e. solid red square in Fig. 3) is systematically higher than that for pp2006 data (black circles in Fig. 3). Note, that there is an agreement between SMD shower shapes for pp2006 data and data-driven gamma-jet simulations if one scales them to the same peak value (Compare red vs. black in upper left plot from Fig. 1 at this link) • Fig. 4, upper left plot (pre1=0 pre2=0): Integrated SMD energy from 25 strips in raw gamma-jet simulations (red line) match pp2006 data (black line) in the region where signal to background ratio is high, E_smd(25-strips)>0.1GeV. This indicates that raw MC does a good job in reproducing total energy deposited by direct photon. • Fig. 5, upper left plot (pre1=0 pre2=0): There is mismatch between distributions of energy in 25 strips cluster from data-driven gamma-jet simulations and pp2006 data. This probably reflects the way we scale our library shower shapes in data-driven shower shape replacement procedure. Currently, the scaling factor for the library shape is calculated based on the ratio of direct photon energy from Geant record to the energy of the library photon. Our library is build out of photons from eta-meson decay, which has been reconstructed by running pi0 finder. The purity of the library is about 70% (see Fig. 1 at this post for more details). The improvement of scaling procedure could be to preserve total SMD energy deposited within 25 strips from raw MC, and use that energy to scale shower shapes from the library. • Fig. 6, upper left plot (pre1=0 pre2=0): Mismatch between integrated 5-strip energy for raw MC and pp2006 in Fig. 6 corresponds to "known" difference in shower shapes from raw Monte-Carlo and real data. Figure 1: SMD shower shapes: data, raw, and data-driven MC (40 strips). Vertical axis shows average energy per strip (no overall shower shapes scaling) Figure 2: Shower shapes: data, raw, and data-driven MC (12 strips) Figure 3: Shower shapes: data, raw, and data-driven MC (5 strips) Figure 4: 25 strips SMD cluster energy for raw Monte-Carlo (Note: type in x-axis lables, should be "25 strip peak" instead of 5) Figure 5: 25 strips SMD cluster energy for data-driven Monte-Carlo Figure 6: 5 strips SMD peak energy for raw Monte-Carlo Figure 7: 5 strips SMD peak energy for data-driven Monte-Carlo Figure 8:Energy from the right tail (up to 30 strips) for raw Monte-Carlo Figure 9:Energy from the right tail (up to 30 strips) for data-driven Monte-Carlo # 2008.10.27 SMD-based shower shape scaling: 25 strips cluster energy, raw vs. data-driven MC Ilya Selyuzhenkov October 27, 2008 Data sets: Shower shapes scaling options in data-driven maker: 1. scale = E_smd^geant / E_smd^library (default) E_smd^geant is SMD energy associated with given photon integrated over +/- 12 strips from raw Monte-Carlo, and E_smd^library is SMD energy from +/- 12 strips for the library photon. 2. scale = E_Geant / E_library (used before in all posts) E_Geant is thrown photon energy from Geant record, and E_library is stand for energy of the library photon. In all figures below (exept for pp2006 data and raw Monte-Carlo) the SMD based shower shape scaling has been used. Figure 1: SMD shower shapes: data, raw, and data-driven MC (40 strips). Vertical axis shows average energy per strip (no overall shower shapes scaling) Figure 2: Shower shapes: data, raw, and data-driven MC (12 strips) Figure 3: Shower shapes: data, raw, and data-driven MC (5 strips) Figure 4: 25 strips SMD cluster energy for data-driven Monte-Carlo (SMD based shower shape scaling) Figure 5: 25 strips SMD cluster energy for raw Monte-Carlo Note, the difference between results in Fig. 4 and 5. for MC gamma-jets (shown in red) at low energy (Esmd < 0.04) for pre1=0 pre2=0 case. This effect is due to the "Number of strips fired in 5-strips cluster > 3" cut. In data-driven Monte-Carlo we may have shower shapes with small number of strips fired (rejected in raw Monte-Carlo) to be replaced by library shape with different (bigger) number of strips fired. This mostly affects photons which starts to shower later in the detector and only fires few strips (pre1=0 pre2=0 case) # 2008.10.30 Various cuts study (pt, Esmd, 8 strips replaced) Ilya Selyuzhenkov October 30, 2008 Below are links to drupal pages with various SMD energy distributions and shower shapes for the following set of cuts/conditions: • Case A: pt > 7 GeV, +/- 12 strips replaced • Case B: pt > 7 GeV, +/- 8 strips replaced • Case C: pt > 7 GeV, +/- 12 strips replaced, E_smd(25strips) > 0.1 • Case D: pt > 8.5 GeV, +/- 12 strips replaced # 2008.10.30 Distance to cut line from sided residual Figure 1: Case A Figure 2:Case B Figure 3:Case C Figure 4:Case D # 2008.10.30 SMD shower shapes: data, raw, and data-driven MC (12 strips) Figure 1: Case A Figure 2:Case B Figure 3:Case C Figure 4:Case D # 2008.10.30 SMD shower shapes: data, raw, and data-driven MC (30 strips) Figure 1: Case A Figure 2: Case B Figure 3: Case C Figure 4: Case D # 2008.10.30 Sided residual Figure 1: Case A Figure 2:Case B Figure 3:Case C Figure 4:Case D # 2008.10.30 Smd emergy for left tail (-3 to -30 strips) Figure 1: Case A Figure 2:Case B Figure 3:Case C Figure 4:Case D # 2008.10.30 Smd emergy for right tail (3 to 30 strips) Figure 1: Case A Figure 2:Case B Figure 3:Case C Figure 4:Case D # 2008.10.30 Smd energy for 25 central strips Figure 1: Case A Figure 2:Case B Figure 3:Case C Figure 4:Case D # 2008.10.30 Smd energy for 5 central strips Figure 1: Case A Figure 2:Case B Figure 3:Case C Figure 4:Case D # 11 Nov ## November 2008 posts # 2008.11.06 Gamma-jet reconstruction with the Endcap EMC (Analysis status update) Ilya Selyuzhenkov November 06, 2008 ## Gamma-jet reconstruction with the Endcap EMC (Analysis status update for Spin PWG) # 2008.11.11 Yields vs. analysis cuts Ilya Selyuzhenkov November 11, 2008 Data sets: Figure 1: Reconstructed gamma pt for di-jet events and Geant cuts: pt_gamma[Geant] > 7GeV and 1.05 < eta_gamma[Geant] < 2.0 Total integral for the histogram is: N_total = 5284 (after weighting different partonic pt bins and scaled to 3.164pb^-1). Compare with number from Jim Sowinski study for Endcap East+West gamma-jet and pt>7 GeV: N_Jim = 5472 ( Jim's numbers are scaled to 3.164pb^-1 : [2539+5936]*3.164/4.9) Figure 2: Reconstructed jet pt for di-jet events and the same cuts as in Fig. 1 ### Yield vs. various analysis cuts List of cuts (sorted by bin number in Figs. 2 and 3): 1. N_events : total number of di-jet events found by the jet-finder 2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi 3. R_{3x3cluster} > 0.9 : Energy in 3x3 cluster of EEMC tower to the total jet energy 4. R_EM^jet < 0.9 : neutral energy fraction cut for on away side jet 5. N_ch=0 : no charge tracks associated with a gamma candidate 6. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap) 7. N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak 8. N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak 9. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc... 10. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster Figure 3: Number of accepted events vs. various analysis cuts The starting number of events (shown in first bin of the plots) is the number of di-jets with reconstructed gamma_pt>7 GeV and jet_pt>5 GeV upper left: cuts applied independently upper right: cuts applied sequentially lower left: ratio of pp2006 data vs. MC sum of gamma-jet and QCD-jets events (cuts applied independently) lower right:ratio of pp2006 data vs. MC sum of gamma-jet and QCD jets events (cuts applied sequentially) Figure 4: Number of accepted events vs. various analysis cuts Data from Fig. 3 (upper plots) scaled to the initial number of events from first bin: left: cuts applied independently right: cuts applied sequentially # 2008.11.18 Cluster isolation cuts: 2x1 vs. 2x2 vs. 3x3 Ilya Selyuzhenkov November 18, 2008 ### Data sets: ### 2x1, 2x2, and 3x3 clusters definition: • 3x3 cluster: tower energy sum for 3x3 patch around highest tower • 2x2 cluster: tower energy sum for 2x2 patch which are closest to 3x3 tower patch centroid. 3x3 tower patch centroid is defined based on tower energies weighted wrt tower centers: centroid = sum{E_tow * r_tow} / sum{E_tow}. Here r_tow=(x_tow, y_tow) denotes tower center. • 2x1 cluster: tower energy sum for high tower plus second highest tower in 3x3 patch • r=0.7 energy is calculated based on towers within a radius of 0.7 (in delta phi and eta) from high tower ### Cuts applied all gamma-jet candidate selection cuts except 3x3/r=0.7 energy isolation cut ### Results for 2x1, 2x2, and 3x3 clusters 1. Energy fraction in NxN cluster in r=0.7 radius 2x1, 2x2, 3x3 patch to jet radius of 0.7 energy ratios 2. Yield vs. NxN cluster energy fraction in r=0.7 For a given cluster energy fraction yield is defined as an integral on the right 3. Efficiency vs. NxN cluster energy fraction in r=0.7 For a given cluster energy fraction efficiency is defined as the yield (on the right) normalized by the total integral (total yield) # Efficiency vs. NxN cluster energy fraction in r=0.7 ## Efficiency vs. NxN cluster energy fraction in r=0.7 Figure 1b: 2x1/0.7 ratio Figure 2b: 2x2/0.7 ratio Figure 3b: 3x3/0.7 ratio Figure 4b: 3x3/0.7 ratio but only using towers which passed jet finder threshold # Energy fraction in NxN cluster within r=0.7 radius ## Energy fraction in NxN cluster within r=0.7 radius Figure 1a: 2x1/0.7 ratio Figure 2a: 2x2/0.7 ratio Figure 3a: 3x3/0.7 ratio Figure 4a: 3x3/0.7 ratio but only using towers which passed jet finder threshold # Yield vs. NxN cluster energy fraction in r=0.7 ## Yield vs. NxN cluster energy fraction in r=0.7 Figure 1c: 2x1/0.7 ratio Figure 2c: 2x2/0.7 ratio Figure 3c: 3x3/0.7 ratio Figure 4c: 3x3/0.7 ratio but only using towers which passed jet finder threshold # 2008.11.21 Energy fraction from 2x1 vs. 2x2 vs. 3x3 or 0.7 radius: rapidity dependence Ilya Selyuzhenkov November 21, 2008 ### Data sets: ### 2x1, 2x2, and 3x3 clusters definition: • 3x3 cluster: tower energy sum for 3x3 patch around highest tower • 2x2 cluster: tower energy sum for 2x2 patch which are closest to 3x3 tower patch centroid. 3x3 tower patch centroid is defined based on tower energies weighted wrt tower centers: centroid = sum{E_tow * r_tow} / sum{E_tow}. Here r_tow=(x_tow, y_tow) denotes tower center. • 2x1 cluster: tower energy sum for high tower plus second highest tower in 3x3 patch • r=0.7 energy is calculated based on towers within a radius of 0.7 (in delta phi and eta) from high tower ### Cuts applied all gamma-jet candidate selection cuts except 3x3/r=0.7 energy isolation cut ### Results There are two sets of figures in links below: • Number of counts for a given energy fraction • Yield above given energy fraction [figures with right integral in the caption] Yield is defined as the integral above given energy fraction up to the maximum value of 1 Gamma candidate detector eta < 1.5 (eta region where we do have most of the TPC tracking): Gamma candidate detector eta > 1.5: (smaller tower size) ### Some observation • For pre1>0 condition (contains most of events) yield in Monte-Carlo for eta > 1.5 case is about factor of two different than that from pp2006 data, while for eta < 1.5 Monte-Carlo yield agrees with data within 10-15%. This could be due to trigger effect? • For pre1=0 case yiled for both eta > 1.5 and eta < 1.5 are different in data and MC This could be due to migration of counts from pre1=0 to pre1>0 in pp2006 data due to more material budget than it is Monte-Carlo • For pre1=0 condition pp2006 data shapes are not reproduced by gamma-jet Monte-Carlo. With a larger cluster size (2x1 -> 3x3) the pp2006 and MC gamma-jet shapes are getting closer to each other. • For pre1>0 condition (with statistics available), pp2006 data shapes are consistent with QCD Monte-Carlo. # Cluster energy fraction in 0.7 radius: detector eta < 1.5 ## Energy fraction in NxN cluster within r=0.7 radius: detector eta < 1.5 Figure 1a: 2x1/0.7 energy fraction [number of counts per given fraction] Figure 2a: 2x2/0.7 energy fraction [number of counts per given fraction] Figure 3a: 3x3/0.7 energy fraction [number of counts per given fraction] ## Yield vs. NxN cluster energy fraction in r=0.7: detector eta < 1.5 Figure 4a: 2x1/0.7 energy fraction [yield] Figure 5a: 2x2/0.7 energy fraction [yield] Figure 6a: 3x3/0.7 energy fraction [yield] # Cluster energy fraction in 0.7 radius: detector eta > 1.5 ## Energy fraction in NxN cluster within r=0.7 radius: detector eta < 1.5 Figure 1a: 2x1/0.7 energy fraction [number of counts per given fraction] Figure 2a: 2x2/0.7 energy fraction [number of counts per given fraction] Figure 3a: 3x3/0.7 energy fraction [number of counts per given fraction] ## Yield vs. NxN cluster energy fraction in r=0.7: detector eta < 1.5 Figure 4a: 2x1/0.7 energy fraction [yield] Figure 5a: 2x2/0.7 energy fraction [yield] Figure 6a: 3x3/0.7 energy fraction [yield] # Cluster energy fraction in 3x3 patch: detector eta < 1.5 ## Energy fraction from NxN cluster in 3x3 patch: detector eta < 1.5 Figure 1a: 2x1/3x3 energy fraction [number of counts per given fraction] Figure 2a: 2x2/3x3 energy fraction [number of counts per given fraction] ## Yield vs. NxN cluster energy fraction in 3x3 patch: detector eta < 1.5 Figure 4a: 2x1/3x3 energy fraction [yield] Figure 5a: 2x2/3x3 energy fraction [yield] # Cluster energy fraction in 3x3 patch: detector eta > 1.5 ## Energy fraction from NxN cluster in 3x3 patch: detector eta > 1.5 Figure 1a: 2x1/3x3 energy fraction [number of counts per given fraction] Figure 2a: 2x2/3x3 energy fraction [number of counts per given fraction] ## Yield vs. NxN cluster energy fraction in 3x3 patch: detector eta > 1.5 Figure 4a: 2x1/3x3 energy fraction [yield] Figure 5a: 2x2/3x3 energy fraction [yield] # 2008.11.25 Yiled vs. analysis cuts: eta dependence Ilya Selyuzhenkov November 25, 2008 ### Data sets: ### Some observation • Fig. 1 [upper&lower left, 3rd bin] indicates that cluster energy isolation is the most important cut for signal/background separation • Fig.1 [lower right, 3rd bin] shows that R_cluster cut is independent from (or orthogonal to) other cuts • Fig.1 [upper&lower left 4th bin] shows that cut on neutral energy fraction for the away side jet rejects more signal that background events We probably need to reconsider that cut • Fig.2 [lower left, 5th bin] shows that charge particle veto significantly improves signal to background ratio • Fig.2 [lower right, 5th bin] shows that charge particle veto also independent from other cuts • Fig.3 [lower left, 5th bin] shows that in the region were we do not have TPC tracking (photon eta > 1.5) charge particle veto is not efficient , although there is still some improvement from this cut. This probably due to tracks with eta <1.5 which fall into large isolation radius r=0.7. ### Yield vs. various analysis cuts List of cuts (sorted according to bin number in Figs. 1-3. [No SMD sided residual cuts]): 1. N_events : total number of di-jet events found by the jet-finder 2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi 3. R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy R_{3x3cluster}>0.9 for Fig. 1, and it is disabled in Fig. 2 and 3 4. R_EM^jet < 0.9 : neutral energy fraction cut for on away side jet 5. N_ch=0 : no charge tracks associated with a gamma candidate 6. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap) 7. N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak 8. N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak 9. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc... 10. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster Figure 1: Number of accepted events vs. various analysis cuts The starting number of events (shown in first bin of the plots) is the number of di-jets with reconstructed gamma_pt>7 GeV and jet_pt>5 GeV upper left: cuts applied independently upper right: expept this cut fired (event passed all other cuts and being rejected by this cut) lower left: "cuts applied independently" normalized by the total number of events lower right: "expept this cut fired" normalized by the total number of events Figure 2: Same as Fig.1 except: no R_cluster cut and photon detector eta < 1.5 (eta region where we do have most of the TPC tracking) Figure 3: Same as Fig.1 except: no R_cluster cut and photon detector eta > 1.5 # 12 Dec ## December 2008 posts # 2008.12.08 Run 8 EEMC QA Ilya Selyuzhenkov December 08, 2008 ### Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) Trigger: eemc-http-mb-L2gamma [id:137641] • pp2008 - STAR 2008 pp data Trigger: etot-mb-l2 [id:7] Days: 53-70; ~0.5M triggered events (1/3 of available statistics) ### Detector subsystems involved in analysis: 1. TPC (vertex, jets, charge particle veto) 2. Endcap EMC (triggering, photon candidate reconstruction) 3. Barrel EMC (away side jet reconstruction) ### Gamma-jet analysis cuts: 1. Select only di-jet events 2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi 3. R_EM^jet < 0.9 : neutral energy fraction cut for the away side jet 4. N_ch=0 : no charge tracks associated with a gamma candidate 5. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap) 6. N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak 7. N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak 8. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc... 9. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster 10. R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy (not applied here) Figure 1: EEMC x vs. y position of photon candidate for 2008 data sample Problem with pre-shower layer in Sector 10 can been seen in the upper left corner Figure 2: EEMC x vs. y position of photon candidate for 2006 data sample Figure 3: Average < E_pre1 * E_pre2 > for 3x3 cluster around high tower vs run number for sectors 9, 10 and 11 Note, zero pre-shower energy for sector 10 (black points) for days 61, 62, 64, and 67. All di-jet events for pp2008 data are shown (no gamma-jet cuts) Figure 3a: Same as Fig.3, zoom into day 61 Figure 3b: Same as Fig.3, zoom into day 62 Figure 3c: Same as Fig.3, zoom into day 64 Figure 3d: Same as Fig.3, zoom into day 67 Figure 4: EEMC x vs. y position of photon candidate for 2008 data sample Same as Fig. 1, but excluding days: 61, 62, 64, and 67 ### Conclusion on QA: No problem with pp2008 data have been found, except that for some runs (mostly on days 61, 62, 64, and 67) EEMC pre-shower layer for sector 10 was off. ### Comparison between 2006 and 2008 data Figure 5: Vertex z distribution: All gamma-jet cuts applied, plus pt_gamma>7 and pt_jet > 5 GeV (exlcuding days 61, 62, 64, and 67) Results are shown for pp2008 data sample (black), vs. pp2006 data (red). pp2008 data scaled to the same total number of candidates as in pp2006 data. Figure 6: Shower shapes within +/- 30 strips from high strip (same cuts as in Fig. 5): Figure 7: Shower shapes within +/- 5 strips from high strip (same cuts as in Fig. 5, no scaling): # 2008.12.09 pp Run 8 vs. Run 6 SMD shower shapes Ilya Selyuzhenkov December 09, 2008 ### Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) Trigger: eemc-http-mb-L2gamma [id:137641] • pp2008 - STAR 2008 pp data Trigger: etot-mb-l2 [id:7] Days: 53-70; ~0.5M triggered events (1/3 of available statistics) # 2008.12.09 pp Run 8 vs. Run 6 SMD shower shapes Ilya Selyuzhenkov December 09, 2008 ### Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) Trigger: eemc-http-mb-L2gamma [id:137641] • pp2008 - STAR 2008 pp data Trigger: etot-mb-l2 [id:7] Days: 53-70; ~0.5M triggered events (1/3 of available statistics) ### Gamma-jet analysis cuts: 1. Select only di-jet events 2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi 3. R_EM^jet < 0.9 : neutral energy fraction cut for the away side jet 4. N_ch=0 : no charge tracks associated with a gamma candidate 5. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap) 6. N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak 7. N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak 8. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc... 9. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster 10. R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy (not applied here) ### Comparison between 2006 and 2008 data Figure 1: Vertex z distribution: All gamma-jet cuts applied, plus pt_gamma>7 and pt_jet > 5 GeV (exlcuding days 61, 62, 64, and 67) Results are shown for pp2008 data sample (black), vs. pp2006 data (red). pp2008 data scaled to the same total number of candidates as in pp2006 data. Figure 2: Shower shapes within +/- 30 strips from high strip (same cuts as in Fig. 1): Figure 3: Shower shapes within +/- 5 strips from high strip (same cuts as in Fig. 1, no scaling): # 2008.12.09 pp Run 8 vs. Run 6 SMD shower shapes Ilya Selyuzhenkov December 09, 2008 ### Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) Trigger: eemc-http-mb-L2gamma [id:137641] • pp2008 - STAR 2008 pp data Trigger: etot-mb-l2 [id:7] Days: 53-70; ~0.5M triggered events (1/3 of available statistics) ### Gamma-jet analysis cuts: 1. Select only di-jet events 2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi 3. R_EM^jet < 0.9 : neutral energy fraction cut for the away side jet 4. N_ch=0 : no charge tracks associated with a gamma candidate 5. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap) 6. N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak 7. N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak 8. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc... 9. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster 10. R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy (not applied here) ### Comparison between 2006 and 2008 data Figure 1: Vertex z distribution: All gamma-jet cuts applied, plus pt_gamma>7 and pt_jet > 5 GeV (exlcuding days 61, 62, 64, and 67) Results are shown for pp2008 data sample (black), vs. pp2006 data (red). pp2008 data scaled to the same total number of candidates as in pp2006 data. Figure 2: Shower shapes within +/- 30 strips from high strip (same cuts as in Fig. 1): Figure 3: Shower shapes within +/- 5 strips from high strip (same cuts as in Fig. 1, no scaling): # 2008.12.09 pp Run 8 vs. Run 6 SMD shower shapes Ilya Selyuzhenkov December 09, 2008 ### Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) Trigger: eemc-http-mb-L2gamma [id:137641] • pp2008 - STAR 2008 pp data Trigger: etot-mb-l2 [id:7] Days: 53-70; ~0.5M triggered events (1/3 of available statistics) ### Gamma-jet analysis cuts: 1. Select only di-jet events 2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi 3. R_EM^jet < 0.9 : neutral energy fraction cut for the away side jet 4. N_ch=0 : no charge tracks associated with a gamma candidate 5. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap) 6. N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak 7. N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak 8. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc... 9. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster 10. R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy (not applied here) ### Comparison between 2006 and 2008 data Figure 1: Vertex z distribution: All gamma-jet cuts applied, plus pt_gamma>7 and pt_jet > 5 GeV (exlcuding days 61, 62, 64, and 67) Results are shown for pp2008 data sample (black), vs. pp2006 data (red). pp2008 data scaled to the same total number of candidates as in pp2006 data. Figure 2: Shower shapes within +/- 30 strips from high strip (same cuts as in Fig. 1): Figure 3: Shower shapes within +/- 5 strips from high strip (same cuts as in Fig. 1, no scaling): # 2008.12.09 pp Run 8 vs. Run 6 SMD shower shapes Ilya Selyuzhenkov December 09, 2008 ### Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) Trigger: eemc-http-mb-L2gamma [id:137641] • pp2008 - STAR 2008 pp data Trigger: etot-mb-l2 [id:7] Days: 53-70; ~0.5M triggered events (1/3 of available statistics) ### Gamma-jet analysis cuts: 1. Select only di-jet events 2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi 3. R_EM^jet < 0.9 : neutral energy fraction cut for the away side jet 4. N_ch=0 : no charge tracks associated with a gamma candidate 5. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap) 6. N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak 7. N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak 8. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc... 9. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster 10. R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy (not applied here) ### Comparison between 2006 and 2008 data Figure 1: Vertex z distribution: All gamma-jet cuts applied, plus pt_gamma>7 and pt_jet > 5 GeV (exlcuding days 61, 62, 64, and 67) Results are shown for pp2008 data sample (black), vs. pp2006 data (red). pp2008 data scaled to the same total number of candidates as in pp2006 data. Figure 2: Shower shapes within +/- 30 strips from high strip (same cuts as in Fig. 1): Figure 3: Shower shapes within +/- 5 strips from high strip (same cuts as in Fig. 1, no scaling): ### Conclusion: # 2008.12.09 pp Run 8 vs. Run 6 shower shapes Ilya Selyuzhenkov December 09, 2008 ### Data sets: • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) Trigger: eemc-http-mb-L2gamma [id:137641] • pp2008 - STAR 2008 pp data Trigger: etot-mb-l2 [id:7] Days: 53-70; ~0.5M triggered events (1/3 of available statistics) ### Gamma-jet analysis cuts: 1. Select only di-jet events 2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi 3. R_EM^jet < 0.9 : neutral energy fraction cut for the away side jet 4. N_ch=0 : no charge tracks associated with a gamma candidate 5. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap) 6. N_(5-strip cluster)^u > 2 : minimum number of strips in EEMC SMD u-plane cluster around peak 7. N_(5-strip cluster)^v > 2 : minimum number of strips in EEMC SMD v-plane cluster around peak 8. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc... 9. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluster 10. R_{3x3cluster}: Energy in 3x3 cluster of EEMC tower to the total jet energy (not applied here) ### Comparison between 2006 and 2008 data Figure 1: Vertex z distribution: All gamma-jet cuts applied, plus pt_gamma>7 and pt_jet > 5 GeV (exlcuding days 61, 62, 64, and 67) Results are shown for pp2008 data sample (black), vs. pp2006 data (red). pp2008 data scaled to the same total number of candidates as in pp2006 data. Figure 2: Shower shapes within +/- 30 strips from high strip (same cuts as in Fig. 1): Figure 3: Shower shapes within +/- 5 strips from high strip (same cuts as in Fig. 1, no scaling): # 2008.12.11 Run 8 EEMC QA (presentation for Spin PWG) Ilya Selyuzhenkov December 11, 2008 ### Run 8 QA with EEMC gamma-jet candidates Presentation in pdf or open office file format # 2008.12.16 Effect of L2gamma trigger in simulations Ilya Selyuzhenkov December 16, 2008 ### Data sets • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) Trigger: eemc-http-mb-L2gamma [id:137641] • gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events. Partonic pt range 2-25 GeV. • QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events. Partonic pt range 2-25 GeV. ### Cuts applied Gamma-jet isolation cuts except 3x3/r=0.7 energy isolation cut ### Data driven shower shape replacement maker fix Figure 1: (reproducing old results with dd-maker fix) transverse momentum and vertex z distributions before (with ideal gains/pedestals) and after (with realistic gains/pedestal tables) dd-maker fix are in a good agreement. For details on "dd-maker problem", read these hyper news threads: emc2:2905, emc2:2900, and phana:294 Now we can run L2gamma trigger emulation and Eemc SMD ddMaker in the same analysis chain. ### l2-gamma trigger effect in simulation Figure 2: Vertex z distribution with and without trigger condition in simulations (emulated trigger: eemc-http-mb-L2gamma [id:137641]). Solid red/green symbols show results with l2gamma condition applied, while red/green lines show results for the same analysis cuts but without trigger condition. Note, good agreement between MC QCD jets with trigger condition on (green solid squared) and pp2006 data (black solid circles) for pre-shower1>0 case. Figure 3: pt distribution with/without trigger condition in simulations. Same color coding as in Fig. 2 Figure 4: Same as Fig. 3 just on a log scale One can clearly see large trigger effect when applied for QCD jet events, and a little effect for direct gammas. Figure 5: gamma candidate pt QCD (right) and prompt photon (left) Monte-Carlo: no (upper) with (lower) L2e-gamma trigger condition No photon pt and no jet pt cuts Figure 6: gamma candidate pt for QCD Monte-Carlo: no L2e-gamma trigger condition No photon pt and no jet pt cuts Figure 7: gamma candidate pt for QCD Monte-Carlo: L2e-gamma trigger condition applied (id:137641) No photon pt and no jet pt cuts # 2008.12.19 Parton pt distribution for Pythia QCD and gamma-jet events Ilya Selyuzhenkov December 19, 2008 ### Data sets ### Cuts applied Gamma-jet isolation cuts except 3x3/r=0.7 energy isolation cut Figure 1: Parton pt distibution for gamma-jet candidates from Pythia QCD sample with various pt and l2gamma trigger conditions Figure 2: Parton pt distibution for gamma-jet candidates from Pythia prompt photon sample with various pt and l2gamma trigger conditions # 2009 ## Year 2009 posts # 01 Jan ## January 2009 posts # 2009.01.08 Away side jet pt vs. photon pt Ilya Selyuzhenkov January 08, 2009 ### Data sets • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) Trigger: eemc-http-mb-L2gamma [id:137641] • gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events. Partonic pt range 2-25 GeV. • QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events. Partonic pt range 2-25 GeV. ### Cuts applied • Di-jet events • Require to reconstruct photon momentum (no gamma-jet isolation cuts) • Gamma pt > 7GeV • L2gamma emulation in Monte-Carlo • L2gamma triggered pp2006 events ### Comments (concentrated on pre-shower1>0 case which has better statistics for QCD Monte-Carlo): • Fig.1, lower plots Vertex z distributions from QCD MC and pp2006 data are different in the negative region • Fig.2, lower plots For the away side jet pt < 8GeV region QCD Monte-Carlo underestimates the data. • Fig.4, lower plots gamma-jet pt asymmetry plot shows that in QCD MC photon and jet pt's are better correlated than in the data • Fig.5, lower plots Most of the differences between QCD MC and pp2006 data for pre-shower1>0 case are probably from the lower gamma and jet pt region ### Figures Figure 1: Vertex z distribution Figure 2: Away side jet pt Figure 3: Photon pt Figure 4: gamma-jet pt asymmetry: (pt_gamma - pt_jet)/pt_gamma Figure 5: gamma pt vs. away side jet pt 1st column: triggered pp2006 data 2nd column: gamma-jet MC (l2gamma trigger on) 3rd column: QCD background MC (l2gamma trigger on) # 2009.01.20 Away side jet pt vs. photon pt: more stats for QCD pt_parton 9-15GeV Ilya Selyuzhenkov January 20, 2009 Note: this is an update with 10x more statitstics for QCD 9-15GeV parton pt bin. See this post for old results. ### Data sets • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) Trigger: eemc-http-mb-L2gamma [id:137641] • gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events. Partonic pt range 2-25 GeV. • QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events. Partonic pt range 2-25 GeV. ### Cuts applied • Di-jet events • Require to reconstruct photon momentum (no gamma-jet isolation cuts) • Gamma pt > 7GeV • L2gamma emulation in Monte-Carlo • L2gamma triggered pp2006 events ### Comments • Vertex z distributions from QCD MC and pp2006 data are different in the negative region (see Fig.1) • pp2006 data to Monte-Carlo ratio does not depends on reconstructed photon pt, but it has some vertex z dependence (see data to MC ratio in Fig.6 for pre-shower1 > 4MeV case) ### Figures Figure 1: Vertex z distribution Figure 2: Away side jet pt Figure 3: Photon pt Figure 4: gamma-jet pt asymmetry: (pt_gamma - pt_jet)/pt_gamma Figure 5: gamma pt vs. away side jet pt 1st column: triggered pp2006 data 2nd column: gamma-jet MC (l2gamma trigger on) 3rd column: QCD background MC (l2gamma trigger on) ### Data to Monte_Carlo normalization Figure 6: pp2006 data to Monte -Carlo sum [QCD + gamma-jet] ratio for pre-shower1>4MeV (most of statistics) Left: data to MC ratio vs. reconstructed gamma pt. Solid line shows constant line fit (p0 ~ 1.3) Right: data to MC ratio vs. reconstructed vertex position # 2009.01.27 gamma and jet pt plots with detector |eta|_jet < 0.8, pt_jet > 7 Ilya Selyuzhenkov January 27, 2009 ### Data sets • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) Trigger: eemc-http-mb-L2gamma [id:137641] • gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events. Partonic pt range 2-25 GeV. • QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events. Partonic pt range 2-25 GeV. ### Cuts applied • Di-jet events • Require to reconstruct photon momentum (no gamma-jet isolation cuts) • jet pt > 7GeV • Gamma pt > 7GeV or no pt cuts • L2gamma emulation in Monte-Carlo • L2gamma triggered pp2006 events • cos (phi_jet - phi_gamma) < -0.8 • detector |eta_jet|< 0.8 • |v_z| < 100 ### Figures All figures: • All pre-shower conditions combined, pre1<10MeV • Left plots: no gamma pt cut Right plots: pt_gamma >7GeV • Thick blue line shows MC sum: QCD + gamma-jet • Thin solid color lines shows distributions from various partonic pt bins for QCD MC See figures legend for color coding Figure 1: Vertex z distribution Figure 2: Photon eta Figure 3: Away side jet eta Figure 4:Photon pt Figure 5: Away side jet pt Figure 6: Away side jet detector eta # 2009.01.27 gamma and jet pt plots with |eta|_jet < 0.7 Ilya Selyuzhenkov January 27, 2009 ### Data sets • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) Trigger: eemc-http-mb-L2gamma [id:137641] • gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events. Partonic pt range 2-25 GeV. • QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events. Partonic pt range 2-25 GeV. ### Cuts applied • Di-jet events • Require to reconstruct photon momentum (no gamma-jet isolation cuts) • Gamma pt > 7GeV or no pt cuts • L2gamma emulation in Monte-Carlo • L2gamma triggered pp2006 events • cos (phi_jet - phi_gamma) < -0.8 • |eta_jet|< 0.7 • |v_z| < 100 ### Figures All figures: • All pre-shower conditions combined, pre1<10MeV • Left plots: no gamma pt cut Right plots: pt_gamma >7GeV • Thick blue line shows MC sum: QCD + gamma-jet • Thin solid color lines shows distributions from various partonic pt bins for QCD MC See figures legend for color coding Figure 1: Vertex z distribution Figure 2: Photon eta Figure 3: Away side jet eta Figure 4:Photon pt Same as in Fig.4 on a log scale: no gamma pt cut and pt>7GeV Figure 5: Away side jet pt Same as in Fig.5 on a log scale: no gamma pt cut and pt>7GeV # 02 Feb ## February 2009 posts # 2009.02.02 No pre-shower cuts, Normalization fudge factor 1.24 Ilya Selyuzhenkov February 02, 2009 ### Data sets • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) Trigger: eemc-http-mb-L2gamma [id:137641] • gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events. Partonic pt range 2-25 GeV. • QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events. Partonic pt range 2-25 GeV. ### Cuts applied • Di-jet events • Require to reconstruct photon momentum (no gamma-jet isolation cuts) • Gamma pt > 7GeV • L2gamma emulation in Monte-Carlo • L2gamma triggered pp2006 events • cos (phi_jet - phi_gamma) < -0.8 • detector |eta_jet|< 0.8 • |v_z| < 100 ### Figures All figures: • All pre-shower conditions combined, No pre-shower cuts • Thick blue line shows MC sum: QCD + gamma-jet • Black solid circles: pp2006 data • Monte-Carlo results first scaled to 3.164 pb^-1 according to Pythia luminosity and then an additional fudge factor of 1.24 has been applied. Fudge factor is defined as the yields ratio from data to scaled with Pythia luminosity Monte-Carlo for pt_jet>7GeV and pt_gamma>7 candidates Figure 1: Vertex z distribution with pt_jet>7 cut (left) and without pt_jet cut (rigth) Figure 2: Photon (left) and away side jet (right) pt Figure 3: Photon detector eta (left) and corrected for vertex eta (right) Figure 4: Away side jet detector eta (left) and corrected for vertex eta (right) Figure 5: Preshower 1 (left) and Pre-shower2 (right) energy # 2009.02.03 No pre-shower cuts, pt_jet >7 vs. No pt_jet cuts Ilya Selyuzhenkov February 03, 2009 ### Data sets • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) Trigger: eemc-http-mb-L2gamma [id:137641] • gamma-jet[gamma-filtered] - data-driven Prompt Photon [p6410EemcGammaFilter] events. Partonic pt range 2-25 GeV. • QCD jets[gamma-filtered] - data-driven QCD [p6410EemcGammaFilter] events. Partonic pt range 2-25 GeV. ### Cuts applied • Di-jet events • Require to reconstruct photon momentum (no gamma-jet isolation cuts) • Gamma pt > 7GeV • L2gamma emulation in Monte-Carlo • L2gamma triggered pp2006 events • cos (phi_jet - phi_gamma) < -0.8 • detector |eta_jet|< 0.8 • |v_z| < 100 ### Figures Each figure has: • All pre-shower conditions combined, No pre-shower cuts • Thick blue line shows MC sum: QCD + gamma-jet • Black solid circles shows pp2006 data • Left plots: pt_jet>7GeV Right plots: no cuts on pt_jet • Monte-Carlo results for QCD and gamma-jet samples are first scaled to 3.164 pb^-1 according to Pythia luminosity, added together, and then an additional fudge factor of 1.24 applied. Fudge factor is defined as pp2006 to Monte-Carlo sum ratio for pt_jet>7GeV and pt_gamma>7 candidates Figure 1: Vertex z distribution Figure 2: Photon detector eta Figure 3: Corrected for vetrex photon eta Figure 4: Away side jet detector eta Figure 5: Corrected for vetrex away side jet eta Figure 6:Photon pt Figure 7: Away side jet pt Figure 8: Pre-shower 1 energy Figure 9: Pre-shower 2 energy # 2009.02.06 Pre-shower energy distribution Run6 vs. Run8 geometry Ilya Selyuzhenkov February 06, 2009 ### Data sets • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) Trigger: eemc-http-mb-L2gamma [id:137641] • mc2006: gamma-jet+QCD jets [p6410EemcGammaFilter] events. • Partonic pt range 2-25 GeV. • pp2008 - STAR 2008 pp data Trigger: etot-mb-l2 [id:7] ### Cuts applied • Di-jet events • Require to reconstruct photon momentum (no gamma-jet isolation cuts) • Gamma pt > 7GeV, jet pt > 7GeV • L2gamma emulation in Monte-Carlo • L2gamma triggered for pp2006 and pp2008 events • cos (phi_jet - phi_gamma) < -0.8 • detector |eta_jet|< 0.8 • |v_z| < 100 ### Figures Each figure has: • All pre-shower conditions combined, No pre-shower cuts • Red circles show pp2006 data • Black triangles show pp2008 data Data scaled to match the integraled yield from pp2006 data • Green line shows MC sum: QCD + gamma-jet Monte-Carlo results for QCD and gamma-jet samples are first scaled to 3.164 pb^-1 according to Pythia luminosity, added together, and then an additional fudge factor of 1.24 applied. Fudge factor is defined as pp2006 to Monte-Carlo sum ratio for pt_jet>7GeV and pt_gamma>7 candidates ### Observations • Pre-shower energy distributions from pp2008 data set are narrower than that for pp2006 data. This corresponds to smaller amount of material budget in y2008 STAR geometry. • Pre-shower energy distribution from Monte-Carlo with y2006 geometry closer follows the distribution from pp2008 data set, rather than that from pp2006 data. This indicates the lack of material budget in y2006 Monte-Carlo. Note: There is a "pre-shower sector 10 problem" for pp2008 data, which results in migration of small fraction of events with pre-shower>0 into pre-shower=0 bin (first zero bins in Fig.1 and 2. below). For pre-shower>0 case this only affects overall normalization of pp2008 data, but not the shape of pre-shower energy distributions. I'm running jet-finder+my software to get more statistics from pp2008 data set, and after more QA will produce list of runs with "pre-shower sector 10 problem", so to exclude them in the next iteration of my plots. Figure 1: Pre-shower1 energy distribution Figure 2: Pre-shower2 energy distribution # 2009.02.09 pp2006, pp2008, amd mc2006 comparison Ilya Selyuzhenkov February 06, 2009 ### Data sets • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) Trigger: eemc-http-mb-L2gamma [id:137641] • mc2006: gamma-jet+QCD jets [p6410EemcGammaFilter] events. • Partonic pt range 2-25 GeV. • pp2008 - STAR 2008 pp data Trigger: etot-mb-l2 [id:7] ### Cuts applied • Di-jet events • Require to reconstruct photon momentum (no gamma-jet isolation cuts) • Gamma pt > 7GeV, jet pt > 7GeV • L2gamma emulation in Monte-Carlo • L2gamma triggered for pp2006 and pp2008 events • cos (phi_jet - phi_gamma) < -0.8 • detector |eta_jet|< 0.8 • |v_z| < 100 ### Figures Each figure has: • All pre-shower conditions combined, No pre-shower cuts • Red circles show pp2006 data • Black triangles show pp2008 data Data scaled to match the integraled yield from pp2006 data • Green line shows MC sum: QCD + gamma-jet Monte-Carlo results for QCD and gamma-jet samples are first scaled to 3.164 pb^-1 according to Pythia luminosity, added together, and then an additional fudge factor of 1.24 applied. Fudge factor is defined as pp2006 to Monte-Carlo sum ratio for pt_jet>7GeV and pt_gamma>7 candidates ### Kinematics Figure 1: vertex z Figure 2: photon detector eta Figure 3: jet detector eta Figure 4: photon pt Figure 5: jet pt Figure 6: gamma-jet pt balance Figure 7: Photon neutral energy fraction Figure 8: Jet neutral energy fraction Figure 9: cos(phi_gamma-phi_jet) ### Photon candidate's 2x1, 2x2, and 3x3 tower cluser energy Figure 10: 3x3 cluster energy Figure 11: 2x1 cluster energy Figure 12: 2x2 cluster energy ### Number of charge tracks, Barrel and Endcap towers within r=0.7 for photon and gamma Figure 13: Number of charged track associated with photon candidate Figure 14: Number of Barrel towers associated with photon candidate Figure 15: Number of Endcap towers associated with photon candidate ### Jet energy composition Figure 16: Jet energy part from Barrel towers Figure 17: Jet energy part from charge tracks # 2009.02.16 pt_jet>5GeV: pre-shower sorting with new normalization Ilya Selyuzhenkov February 16, 2009 ### Data sets • pp2006 - STAR 2006 pp longitudinal data (~ 3.164 pb^1) Trigger: eemc-http-mb-L2gamma [id:137641] • mc2006: gamma-jet+QCD jets [p6410EemcGammaFilter] events. • Partonic pt range 2-25 GeV. • pp2008 - STAR 2008 pp data Trigger: etot-mb-l2 [id:7] ### Cuts applied • Di-jet events • Require to reconstruct photon momentum (no gamma-jet isolation cuts) • Gamma pt > 7GeV, jet pt > 7GeV • L2gamma emulation in Monte-Carlo • L2gamma triggered for pp2006 and pp2008 events • cos (phi_jet - phi_gamma) < -0.8 • detector |eta_jet|< 0.8 • |v_z| < 100 ### Figures Each figure has: • pp2008 data scaled to match the integraled yield from pp2006 data • mc2006 stand for MC sum: QCD + gamma-jet Monte-Carlo results for QCD and gamma-jet samples are first scaled to 3.164 pb^-1 according to Pythia luminosity, added together, and then an additional fudge factor of 1.24 applied. Fudge factor is defined as pp2006 to Monte-Carlo sum ratio for pt_jet>7GeV and pt_gamma>7 candidates ### plots for pt_gamma>7GeV, pt_jet > 5GeV 1. All pre-shower combined: 1D distributions 2. All pre-shower combined: 2D correlations 3. Pre-shower sorting 1D distributions # 2009.02.19 Photon-jet analysis status update for Spin PWG ### Photon-jet analysis status update for Spin PWG (February 19, 2009) Slides: download pdf Previous versions: v1, v2 Link for CIPANP abstract # CIPANP 2009 abstract on photon-jet measurement ### CIPANP 2009 abstract on photon-jet study Title: "Photon-jet coincidence measurements in polarized pp collisions at sqrt{s}=200GeV with the STAR Endcap Calorimeter" Abstract: download pdf Previous versions: v1, v2, v3, v4 Conference link: CIPANP 2009 # 03 Mar ## March 2009 posts # 2009.03.02 Application of the neural network for the cut optimization (zero try) ### Multilayer perceptron (feedforward neural networks) Multilayer perceptron (MLP) is feedforward neural networks trained with the standard backpropagation algorithm. They are supervised networks so they require a desired response to be trained. They learn how to transform input data into a desired response, so they are widely used for pattern classification. With one or two hidden layers, they can approximate virtually any input-output map. They have been shown to approximate the performance of optimal statistical classifiers in difficult problems. ### ROOT implementation for Multilayer perceptron TMultiLayerPerceptron class in ROOT mlpHiggs.C example ### Application for cuts optimization in the gamma-jet analysis Netwrok structure: r3x3, (pt_gamma-pt_jet)/pt_gamma, nCharge, bBtow, eTow2x1: 10 hidden layers: one output later Figure 1: • Upper left: Learning curve (error vs. number of training) Learing method is: Steepest descent with fixed step size (batch learning) • Upper right: Differences (how important are initial variableles for signal/background separation) • Lower left: Network structure (ling thinkness corresponds to relative weight value) • Lower right: Network output. Red - MC gamma-jets, blue QCD background, black pp2006 data Figure 2: Input parameters vs. network output Row: 1: MC QCD, 2: gamma-jet, 3 pp2006 data Vertical axis: r3x3, (pt_gamma-pt_jet)/pt_gamma, nCharge, bBtow, eTow2x1 Horisontal axis: network output Figure 3: Same as Fig. 2 on a linear scale # 2009.03.09 Application of the LDA and MLP classifiers for the cut optimization ### Cut optimization with Fisher's LDA and MLP (neural network) classifiers ROOT implementation for LDA and MLP: ### Application for cuts optimization in the gamma-jet analysis LDA configuration: default MLP configuration: • 2 hidden layers [N+1:N neural network configuration, N is number of input parameters] • Learning method: stochastic minimization (1000 learning cycles) Input parameters (same for both LDA and MLP): 1. Energy fraction in 3x3 cluster within a r=0.7 radius: r3x3 2. Photon-jet pt balance: [pt_gamma-pt_jet]/pt_gamma 3. Number of charge tracks within r=0.7 around gamma candidate 4. Number of Endcap towers fired within r=0.7 around gamma candidate 5. Number of Barrel towers fired within r=0.7 around gamma candidate Figure 1: Signal efficiency and purity, background rejection (left), and significance: Sig/sqrt[Sig+Bg] (right) vs. LDA (upper plots) and MLP (lower plots) classifier discriminants Figure 2: 1. Upper left: Rejection vs. efficiency 2. Upper right: Purity vs. efficiency 3. Lower left: Purity vs. Rejection 4. Lower right: Significance vs. efficiency Figure 3: Data to Monte-Carlo comparison for LDA (upper plots) and MLP (lower plots) Good (within ~ 10%) match between data nad Monte-Carlo a) up to 0.8 for LDA discriminant, and b) up to -0.7 for MLP. Figure 4: Data to Monte-Carlo comparison for input parameters from left to right 1) pt_gamma 2) pt_jet 3) r3x3 4) gamma-jet pt balance 5) N_ch[gamma] 6) N_eTow[gamma] 7) N_bTow[gamma] Colour coding: black pp2006 data, red gamma-jet MC, green QCD MC, blue gamma-jet+QCD Figure 5: Data to Monte-Carlo comparison: correlations between input variables (in the same order as in Fig. 4) and LDA classifier discriminant (horizontal axis). 1st raw: QCD MC; 2nd: gamma-jet MC; 3rd: pp2006 data; 4th: QCD+gamma-jet MC Figure 6: Same as Fig. 6 for MLP discriminant # 2009.03.26 Endcap photon-jet update at the STAR Collaboration meeting Endcap photon-jet update at the STAR Collaboration meeting # 04 Apr April 2009 posts # 2009.04.17 WSU nuclear seminar The STAR spin program with longitudinally polarized proton beams # 2009.04.21 Adding SMD info to the LDA ### Cut optimization with Fisher's LDA classifier ROOT implementation for LDA: ### Application for cuts optimization in the gamma-jet analysis LDA configuration: default LDA input parameters: 1. Energy fraction in 3x3 cluster within a r=0.7 radius: r3x3 2. Photon-jet pt balance: [pt_gamma-pt_jet]/pt_gamma 3. Number of charge tracks within r=0.7 around gamma candidate 4. Number of Endcap towers fired within r=0.7 around gamma candidate 5. Number of Barrel towers fired within r=0.7 around gamma candidate Figure 1: LDA discriminant (no SMD involved in training) Figure 2: LDA (no SMD): Efficiency, rejection, purity vs. discriminant Figure 3: SMD energy in 25 central strips (LDA-dsicriminant>0, no pre-shower1 cut) Figure 4: SMD energy in 25 central strips (LDA-dsicriminant>0, pre-shower1 < 10MeV) Figure 5: Maximum residual (LDA-dsicriminant>0, no pre-shower1 cut) Figure 6: Maximum residual (LDA-dsicriminant>0, pre-shower1 < 10MeV) ### LDA+ SMD analysis SMD info added: a) energy in 5 central srtips b) maximum sided residual Figure 7:LDA with SMD: Efficiency, rejection, purity vs. LDA discriminant Figure 8: LDA discriminant with SMD Figure 9: Maximum residual (SMD LDA-dsicriminant>0, pre-shower1 < 10MeV) ### LDA with and without SMD comparison Figure 10:LDA (no SMD): Efficiency, rejection, purity plots Figure 11: LDA with SMD: Efficiency, rejection, purity plots # 2009.04.28 LDA plus SMD analysis with pre-shower sorting ### Cut optimization with Fisher's LDA classifier ROOT implementation for LDA: ### Application for cuts optimization in the gamma-jet analysis LDA configuration: default LDA input parameters (includes SMD inromation of the distance from max sided residual plot): 1. Energy fraction in 3x3 cluster within a r=0.7 radius: r3x3 2. Photon-jet pt balance: [pt_gamma-pt_jet]/pt_gamma 3. Number of charge tracks within r=0.7 around gamma candidate 4. Number of Endcap towers fired within r=0.7 around gamma candidate 5. Number of Barrel towers fired within r=0.7 around gamma candidate 6. Distance to 80% cut line (see this link for more details) The number of strips in SMD u or v planes is required to be greater than 3 Figure 1: SMD energy in 25 central strips sorted by pre-shower energy 1. Upper left: pre1=0, pre2=0 2. Upper right: pre1=0, pre2>0 3. Lower left: 0<4MeV 4. Lower right: 4<10MeV Right plot for each pre-shower condition shows the ratio of pp2006 data to sum of the Monte-Carlo samples Colour coding: black pp2006 data, red gamma-jet MC, green QCD MC, blue gamma-jet+QCD (combined plot for all pre-shoer bins can be found here) Figure 2: SMD energy in 5 central strips sorted by pre-shower energy (combined plot can be found here) Figure 3: Maximum residual sorted by pre-shower energy (combined plot can be found here) Figure 4: LDA discriminant. Note: LDA algo trained for each pre-shower condition independently Figure 5: LDA: Efficiency, rejection, purity vs. discriminant, sorted by pre-shower energy Figure 6: LDA: Efficiency, rejection, purity plots sorted by pre-shower energy For each pre-shower condition each plot has 4 figures: 1. u-left: rejection vs. efficiency 2. u-right: purity vs. efficiency 3. l-left: purity vs. rejection 4. l-right: significance (signal/sqrt{signal+background}) vs. efficiency # 05 May ### May 2009 posts # 2009.05.03 LDA: varying pt and eta cut ### Cut optimization with Fisher's LDA classifier ROOT implementation for LDA: ### Application for cuts optimization in the gamma-jet analysis LDA configuration: default LDA input parameters Set0: 1. Set0: • Energy fraction in 3x3 cluster within a r=0.7 radius: E_3x3/E_0.7 • Photon-jet pt balance: [pt_gamma-pt_jet]/pt_gamma • Number of charge tracks within r=0.7 around gamma candidate: Ncharge • Number of Endcap towersL fired within r=0.7 around gamma candidate: NtowBarrel • Number of Barrel towers fired within r=0.7 around gamma candidate NtowEndcap 2. Set1: 3. Set2: • All from Set1 • Energy fraction in E_2x1 and E_2x2 witin E_3x3: E_2x1/E_2x2 and E_2x2/E_3x3 4. Set3: • All from Set2 • Energy in post-shower layer under 3x3 tower patch: E_post^3x3 The number of strips in SMD u or v planes is required to be greater than 3 Pre-shower sorting (energy in tiles under 3x3 tower patch): 1. pre1=0, pre2=0 2. pre1=0, pre2>0 3. 0 < pre1 < 0.004 4. 0.004 < pre1 < 0.01 5. pre1 < 0.01 6. pre1 >= 0.01 Photon pt and rapidity cuts: 1. pt>7GeV 2. pt>8GeV 3. pt>9GeV 4. pt>10GeV 5. detector eta <1.4 (pt>7GeV) 6. detector eta > 1.4 (pt>7GeV) Figure 0: photon pt distribution for pre-shower1<0.01 Colour coding: black pp2006 data, red gamma-jet MC, green QCD MC, blue gamma-jet+QCD ### LDA Set0 Figure 1: LDA discriminant with Set0: Data to Monte-Carlo comparison (pt>7GeV cut) Right plot for each pre-shower condition shows the ratio of pp2006 data to sum of the Monte-Carlo samples Colour coding: black pp2006 data, red gamma-jet MC, green QCD MC, blue gamma-jet+QCD Figure 2: efficiency, purity, rejection vs. LDA discriminant (pt>7GeV cut) Figure 3: rejection vs. efficiency Figure 4: purity vs. efficiency Figure 5: purity vs. rejection ### LDA Set1 Figure 6: LDA discriminant with Set1: Data to Monte-Carlo comparison Figure 7: rejection vs. efficiency Figure 8: purity vs. efficiency Figure 9: purity vs. rejection (click link to see the figure) ### LDA Set2 Figure 10: rejection vs. efficiency (click link to see the figure) Figure 11: purity vs. efficiency Figure 12: purity vs. rejection (click link to see the figure) ### LDA Set3 Figure 13: rejection vs. efficiency (click link to see the figure) Figure 14: purity vs. efficiency Figure 15: purity vs. rejection (click link to see the figure) # 2009.05.04 LDA: More SMD info, 3x3 tower energy, correlation matrix ### Cut optimization with Fisher's LDA classifier ROOT implementation for LDA: ### Application for cuts optimization in the gamma-jet analysis LDA configuration: default LDA input parameters Set0: 1. Set4 (link for results with LDA Set0-Set3): • Energy fraction in 3x3 cluster within a r=0.7 radius: E_3x3/E_0.7 • Photon-jet pt balance: [pt_gamma-pt_jet]/pt_gamma • Number of charge tracks within r=0.7 around gamma candidate: Ncharge • Number of Endcap towersL fired within r=0.7 around gamma candidate: NtowBarrel • Number of Barrel towers fired within r=0.7 around gamma candidate NtowEndcap • Shower shape analysis: distance to 80% cut line: distance to cut line • Energy fraction in E_2x1 and E_2x2 witin E_3x3: E_2x1/E_2x2 and E_2x2/E_3x3 • Energy in post-shower layer under 3x3 tower patch: E_post^3x3 • Tower energy in 3x3 patch: E_tow^3x3 • SMD-u energy in 25 central strips: E_smd-u^25 • SMD-v energy in 25 central strips: E_smd-v^25 • SMD-v peak energy (in 5 central strips): E_peak The number of strips in SMD u or v planes is required to be greater than 3 Pre-shower sorting (energy in tiles under 3x3 tower patch): 1. pre1=0, pre2=0 2. pre1=0, pre2>0 3. 0 < pre1 < 0.004 4. 0.004 < pre1 < 0.01 5. pre1 < 0.01 6. pre1 >= 0.01 Integrated yields per pre-shower bin:  sample total integral pre1=0,pre2=0 pre1=0, pre2>0 0 < pre1 < 0.004 0.004 < pre1 < 0.01 pre1 < 0.01 pre1 >= 0.01 photon-jet 2.5640e+03 3.5034e+02 5.2041e+02 5.6741e+02 5.2619e+02 1.9644e+03 5.9994e+02 QCD 5.6345e+04 1.3515e+03 4.3010e+03 1.2289e+04 1.5759e+04 3.3701e+04 2.2644e+04 pp2006 6.2811e+04 6.8000e+02 2.4310e+03 1.2195e+04 1.6766e+04 3.2072e+04 3.0739e+04 Photon pt and rapidity cuts: 1. pt>7GeV 2. pt>8GeV 3. pt>9GeV 4. pt>10GeV 5. detector eta <1.4 (pt>7GeV) 6. detector eta > 1.4 (pt>7GeV) ### LDA Set4 Figure 1: LDA discriminant with Set0: Data to Monte-Carlo comparison (pt>7GeV cut) Right plot for each pre-shower condition shows the ratio of pp2006 data to sum of the Monte-Carlo samples Colour coding: black pp2006 data, red gamma-jet MC, green QCD MC, blue gamma-jet+QCD Figure 2: rejection vs. efficiency Figure 3: purity vs. efficiency Figure 4: purity vs. rejection Figure 5: Correlation matrix (pt>7GeV cut) pre1=0, pre2=0 pre1=0, pre2>0 0 < pre1 < 0.004 0.004 < pre1 < 0.01 pre1 < 0.01 pre1 >= 0.01 # 2009.05.06 Applying cuts on LDA: request minimum purity or efficiency ### Cut optimization with Fisher's LDA classifier For this post LDA input parameters Set4 has been used LDA for various pre-shower bins is trained independetly, and later results with pre-shower1<0.01 are combined. There are a set of plots for various photon pt cuts (pt> 7, 8, 9 10 GeV) and with different selection of cutoff for LDA (either based on purity or efficiency). Number in brackets shows the total yield for the sample. Link to all plots (16 total) as a single pdf file ### pt > 7GeV Figure 1: pt > 7GeV, efficiency@70 Figure 2: pt > 7GeV, purity@35 Figure 3: pt > 7GeV, purity@40 Figure 4: pt > 7GeV, purity@25 (Note: very similar to results with efficiency@70) ### pt > 9GeV Figure 5: pt > 9GeV, efficiency@70 Figure 6: pt > 9GeV, purity@35 ### pt > 10GeV Figure 7: pt > 10GeV, efficiency@70 Figure 8: pt > 10GeV, purity@40 # 2009.05.07 Photon-jets analysis with the Endcap Calorimeter ### Photon-jets with the Endcap Calorimeter (analysis status update for Spin PWG) Slides in pdf format: # 2009.05.12 Variable distributions after LDA at 70% efficiency ### Cut optimization with Fisher's LDA classifier For this post LDA results with Set1 and Set2 has been used Note, that LDA for various pre-shower bins is trained independetly pdf-links with results for pre1=0 and pre2=0 (pre-shower bin 1): Figures below are for 0.004<pre-shower1<0.01 (pre-shower bin 4). Photon pt cut: pt> 7, pre-shower bin: 0.004 < pre1 < 0.01 LDA cut with efficiency @ 70% ### Set1 vs. Set2 What is added in Set2 compared to Set1: smaller cluster size information (r2x1, r2x2), post-shower energy Figure 1: r2x1 before LDA cut LDA cut for Set1 LDA cut for Set2 Figure 2: r2x2 before LDA cut LDA cut for Set1 LDA cut for Set2 Figure 3: r3x3 before LDA cut LDA cut for Set1 LDA cut for Set2 Figure 4: Residual distance before LDA cut LDA cut for Set1 LDA cut for Set2 ### Other variables with LDA Set2 cut Note: Only plos for LDA cut @70 efficiency for Set2 are shown Figure : number of charge particles around photon Figure 5: number of EEMC tower around photon Figure 6: number of BEMC tower around photon Figure 7: photon-jet pt balance Figure 8: SMD energy in 5 centrapl strips Figure 9: SMD energy in 25 central strips: u and v plane separately (plot for V plane) Figure 10: 2x1 cluster energy Figure 11: 2x2 cluster energy Figure 12: 3x3 cluster energy Figure 13: tower energy in r=0.7 radius Figure 14: 3x3 pre-shower1 energy Figure 15: 3x3 pre-shower2 energy Figure 16: 3x3 post-shower energy Figure 17: photon pt Figure 18: jet pt Figure 19: z vertex # 2009.05.31 CIPANP 2009 photon-jet presentation ### CIPANP 2009 presentation on photon-jet study Title: "Photon-jet coincidence measurements in polarized pp collisions at sqrt{s}=200GeV with the STAR Endcap Calorimeter" # 06 Jun ### June 2009 posts # 2009.06.22 CIPANP 2009 photon-jet proceedings ### CIPANP 2009 proceedings on photon-jet study Title: "Photon-jet coincidence measurements in polarized pp collisions at sqrt{s}=200 GeV with the STAR Endcap Calorimeter" # 07 Jul ### July 2009 posts # 2009.07.21 EEMC tower response in Monte-Carlo Data set and cuts: 1. gamma-jet filtered Monte-Carlo 2. Di-jet events from the jet finder (jets threshold: 3.5 GeV) 3. parton pt bin 3-4 GeV (see pt_gamma distributions for various parton pt bins) 4. Thrown photon pseudo-rapidity: eta in [1-2] range 5. Requires to reconstruct photon candidate in the EEMC Figure 1: Average ratio: pt_true / (pt_reco/1.3) vs. pt_reco (GeV/c) • Introduce 1.3 factor here to remove the effect of the fudge factor in slow simulator • Since a limited partonic pt range (3-4 GeV) is used for this study, there is an "artificial" increase of the plotted ratio in pt_gamma > 6 GeV range • Fig. 1 reflects similar features (over a limited pt range) as those found by Hal in his single photon study (see slide 6 of SimulationStudies.ppt presentation) Figure 2: Average momentum difference: pt_true - (pt_reco/1.3) vs. pt_reco (GeV/c) • Fig. 2 shows that on average in GEANT Monte-Carlo we miss ~1GeV independent on the photon pt. EEMC detector response can be still linear even if the ratio in Fig. 1 is not flat. • Usage of fixed 1.3 (or others, like 1.25) fudge factors are not justified. • It seems that using pt-dependent fudge factor (like it is done in this Jason's study) is also unjustified, since the same effects (flat ratio of pt_reco/pt_true ~ 1) can be reached by subtracting 1 GeV from the cluster energy (See Fig. 3). Figure 3: Average ratio: (pt_true -1.06) (pt_reco/1.3) vs. pt_reco (GeV/c) Similar to Fig. 1, but with the true photon pt reduced by 1.06 GeV Resulting true/reco pt ratio is flat in 4-6 GeV range. Before further pursuing our efforts in tuning the tower energy response in the Monte-Carlo, needs to address the observed energy loss difference in the fisrt layer of the BEMC/EEMC detector. See Jason's blog post from 2009.07.16 for more details: Comparison muon energy deposit in the 1st BEMC/EEMC layers # 08 Aug ### August 2009 posts # 2009.08.24 Test of corrected EEMC geometry ### Test of corrected EEMC geometry (bug 1618) Monte-Carlo setup: • One particle per event (photons, electrons, and pions) • Full STAR 2006 geometry. In Kumac file: detp geom y2006g; gexecSTAR_LIB/geometry.so
• Flat in eta (1.08-2.0), phi (0,2pi), and pt (3-30 GeV)
• Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain)
what assumes fixed sampling fraction of 0.05 (5%)

Some definitions:

• Et correction factor : average p_T^thrown / E_T^{reco}.
E_T^{reco} is the total energy in the Endcap Calorimeter (from A2Emaker)
• Sampling fraction: average 0.05 * Energy^{reco} / Energy^thrown.
• SMD energy: average energy in all strips fired (u-plane used for this post)
• Number of SMD strips fired: average total number of strips fired (u-plane used for this post)

Notations used in the plots:

• Left plots: no cAir fix
• Right plots: cAir-fixed
• Photons: black
• Electrons: red
• Pions: green

### Et correction

Note: compare "Left" plots with Brians old results

Figure 1a: Et correction factor vs. pt thrown

Figure 1b: Et correction factor vs. eta thrown

Figure 1c: Et correction factor vs. phi thrown

### Sampling fraction

Note: compare "Right" plots with Jason results with EEMC only geometry

Figure 2a: Sampling fraction vs. pt thrown

Figure 2b: Sampling fraction vs. energy thrown

Figure 2c: Sampling fraction vs. eta thrown

Figure 2d: Sampling fraction vs. phi thrown

### SMD energy

Figure 3a: SMD energy vs. energy thrown

Figure 3b: SMD energy vs. eta thrown

### Number of SMD strips fired

Figure 4a: Number of SMD strips fired vs. energy thrown

Figure 4b: Number of SMD strips fired vs. eta thrown

# 2009.08.25 Test of corrected EEMC geometry: shower shapes

### Test of corrected EEMC geometry (bug 1618)

Monte-Carlo setup is desribed here

• One particle per event (photons, electrons, and pions)
• Full STAR 2006 geometry.
In Kumac file: detp geom y2006g; gexec $STAR_LIB/geometry.so • Flat in eta (1.08-2.0), phi (0,2pi), and pt (3-30 GeV) • Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain) what assumes fixed sampling fraction of 0.05 (5%) • Vertex z=0 • ~50K/per particle type • Non-zero energy: 3 sigma above pedestal Figure 1:Single photon shower shape before (red) and after (black) EEMC cAir bug fixed pt=7-8GeV, eta=1.2-1.4 (left), eta=1.6-1.8 (right) Figure 2: Single photon shower shape vs. data Monte-Carlo: pt=7-10GeV, eta=1.6-1.8 data: no pre-shower1,2; pt_photon>7, pt_jet>5. no eta cuts. (see Fig. 1 from here for other pre-shower conditions) # 2009.08.27 fixed EEMC geometry: pre-shower sorted shower shapes & eta-meson comparison ### Test of corrected EEMC geometry: shower shapes (bug 1618) Monte-Carlo setup is desribed here • One particle per event (photons, electrons, and pions) • Full STAR 2006 geometry. In Kumac file: detp geom y2006g; gexec$STAR_LIB/geometry.so
• Flat in eta (1.08-2.0), phi (0,2pi), and pt (3-30 GeV)
• Using A2Emaker to get reconstructed Tower/SMD energy (no EEMC SlowSimulator in chain)
what assumes fixed sampling fraction of 0.05 (5%)
• Vertex z=0
• ~50K/per particle type
• Non-zero energy: 3 sigma above pedestal

Color coding:

• Black - photon (single particle/event MC)
• Red - electron (single particle/event MC)
• Green - neutral pion (single particle/event MC)
• Blue - photons from eta-meson decay (real data)

Single particle shower shape before (left) and after (right) EEMC cAir bug fixed
Single particle kinematic cuts: pt=7-8GeV, eta=1.2-1.4
Eta-meson shower shapes (blue) taken from Fig. 1 from here of this post
All shapes are normalized to 1 at peak (central strip).

Figure 1: Pre-shower bin 0: E_pre1=0; E_pre2=0

Figure 2: Pre-shower bin 1: E_pre1=0; E_pre2>0

Figure 3: Pre-shower bin 2: E_pre1>0; E_pre1<0.004

Figure 4: Pre-shower bin 3: E_pre1>0.004; E_pre1<0.01

### Shower shape ratios

Results only for corrected EEMC geometry
All shapes are divided by MC single-photon shower shape.

Figure 5a: Pre-shower bin 0: E_pre1=0; E_pre2=0

Figure 5b: Pre-shower bin 1: E_pre1=0; E_pre2>0

Figure 5c: Pre-shower bin 2: E_pre1>0; E_pre1<0.004

Figure 5d: Pre-shower bin 3: E_pre1>0.004; E_pre1<0.01

Figure 6: Single photon to eta-meson shape ratios only (with error bars):
Pre-shower bins 0 (upper-left),1 (upper-right),2 (lower-left), and 3 (lower-right)

# Extracting gamma-jet cross section at forward rapidity from pp@200GeV collisions

### Analysis overview

1. Data samples, event selection, luminosity determination
2. Isolating photon-jet events
• Transverse shower shape analysis
• Isolation cuts
• Cut optimization
3. Trigger effects study
4. Data to Monte-Carlo comparison/normalization and raw yields
5. Acceptance/efficiency corrections
6. Corrected yields
7. Background subtraction
8. Systematic uncertainties
9. Comparison with theory

### Data samples, event selection, luminosity determination

Real data, and signal/background Monte-Carlo samples:

• pp@200GeV collisions, STAR produnctionLong.
Trigger: eemc-http-mb-L2gamma [id:137641] (L ~ 3.164 pb^1)

• Pythia prompt photon (signal) Monte-Carlo sample.
Filtered Prompt Photon p6410EemcGammaFilter.
Partonic pt range 2-25 GeV.

• Pythia 2->2 hard QCD processes (background) Monte-Carlo sample.
Filtered QCD p6410EemcGammaFilter.
Partonic pt range 2-25 GeV.

### Isolating photon-jet events

1. Shower shape analysis
2. Isolation cuts
3. Cut optimization with LDA.
Input variables (list can be expanded):
• Energy fraction in 3x3 cluster within a r=0.7 radius, E_3x3/E_0.7
• Photon-jet pt balance, [pt_gamma-pt_jet]/pt_gamma
• Number of charge tracks within r=0.7 around gamma candidate
• Number of Endcap towers fired within r=0.7 around gamma candidate
• Number of Barrel towers fired within r=0.7 around gamma candidate
• Shower shape analysis: distance to 80% cut line
• Energy fraction in E_2x1 and E_2x2 witin E_3x3
• Energy in post-shower layer under 3x3 tower patch
• Tower energy in 3x3 patch
• SMD-u/v energy in 25 central strips
• SMD-u/v peak energy (in 5 central strips)

### Trigger effects study

No studies yet

• Trigger effects vs pt
• Trigger effects vs eta
• What else?

### Data to Monte-Carlo comparison/normalization and raw yields

• Overall data to MC normalization based on vertex z distribution
• Data to MC comparison of raw yield in various detector subsystems
• Uncorrected yields optimized with different efficiency/purity

### Acceptance/efficiency corrections

No studies yet

• What needs to be studied for acceptance/efficiency effects?
• Converting reconstructed photon (jet) energy/momentum to the true one
• Reconstruction efficiency vs. rapidity, pt, etc

### Corrected yields

No studies yet

• Produce acceptance/efficiency corrected yields

### Background subtraction

No studies yet

• Statistical background subtraction based on Pythia+GEANT Monte-Carlo
• Estimate systematic uncertainties due to background subtraction

### Systematic uncertainties

No studies yet

• Calorimeter energy resolution
• Trigger bias
• Other effects

### Comparison with theory

No comparison yet

• Request for pQCD calculations at forward rapidity

# 2009.09.04 Test of corrected EEMC geometry: SVT, slow-simulator on/off, pre-shower migration

### Test of corrected EEMC geometry: shower shapes (bug 1618)

Monte-Carlo setup:

• One particle per event (photons, electrons, pions, and eta-meson)
• Full STAR 2006 geometry (with/without SVT)
In Kumac file: detp geom y2006g; gexec $STAR_LIB/geometry.so (remove SVT with SVTT_OFF option) • Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and pt (6-10 GeV) • Using A2Emaker to get reconstructed Tower/SMD energy (with/without EEMC SlowSimulator in chain) • Vertex z=0 • ~50K/per particle type • Non-zero energy: 3 sigma above pedestal Color coding: • Photon (single particle MC) • Electron (single particle MC) • Neutral pion (single particle MC) • Eta-meson (single particle MC) • Eta-meson [pp2006 data] (single photons from eta-meson decay) Pre-shower bins: 1. Ep1 = 0, Ep2 = 0 (no energy in both EEMC pre-shower layers) 2. Ep1 = 0, Ep2 > 0 3. 0 < Ep1 < 4 MeV 4. 4 < Ep1 < 10 MeV 5. Ep1 > 10 MeV 6. All pre-shower bins combined Ep1/Ep2 is the energy deposited in the 1st/2nd EEMC pre-shower layer. For a single particle MC it is a sum over all pre-shower tiles in the EEMC with energy of 3 sigma above pedestal. For eta-meson from pp2006 data the sum is over 3x3 tower patch ### Shower shapes Single particle kinematic cuts: pt=7-8GeV, eta=1.2-1.4 Eta-meson shower shapes (blue) taken from Fig. 1 from here of this post All shapes are normalized to 1 at peak (central strip) Figure 1: Shower shape sorted by pre-shower conditions. cAir-Fixed EEMC geometry (NO slow simulator, WITH SVT) Ratio plot Figure 2: Shower shape sorted by pre-shower conditions. cAir-Fixed EEMC geometry (NO slow simulator, NO SVT) Ratio plot Figure 3: Shower shape sorted by pre-shower conditions. cAir-Fixed EEMC geometry (WITH slow simulator, WITH SVT) Ratio plot Figure 4: Shower shape sorted by pre-shower conditions. Old cAir-bug EEMC geometry (NO slow simulator, WITH SVT) Click here to see the plot ### Pre-shower migration with/without SVT Starting with a fixed (50K events) for each type of particle. Change in number of counts for a given pre-shower bin with different detector configuration shows pre-shower migration Figure 5: Pre-shower migration. cAir-Fixed EEMC geometry (WITH SVT) Figure 6: Pre-shower migration. cAir-Fixed EEMC geometry (WITHOUT SVT) ### Sampling fraction with/without Slow-simulator Figure 7: Sampling fraction (0.05 E_reco / E_thrown). cAir-Fixed EEMC geometry (WITHOUT Slow-simulator) Figure 8: Sampling fraction (0.05 E_reco / E_thrown). cAir-Fixed EEMC geometry (WITH Slow-simulator) Slow simulator introduce some non-linearity in the sampling fraction Figure 9: Sampling fraction (0.05 E_reco / E_thrown). cAir-Fixed EEMC geometry (WITHOUT SVT, WITHOUT Slow-simulator) Click here to see the plot Figure 10: Sampling fraction (0.05 E_reco / E_thrown). Old cAir-bug EEMC geometry (NO slow simulator, WITH SVT) Click here to see the plot # 2009.09.11 Test of corrected EEMC geometry: LOW_EM cuts ### Test of corrected EEMC geometry: SVT and LOW_EM cuts Monte-Carlo setup: • One particle per event (only photons in this post) • Full STAR 2006 geometry (with/without SVT, LOW_EM cuts) In Kumac file: detp geom y2006g; gexec$STAR_LIB/geometry.so (vary SVTT_OFF, LOW_EM)
LOW_EM cut definition is given at the end of this page
• Throw particles flat in eta (1.2, 1.9), phi (0, 2pi), and pt (6-10 GeV)
• Using A2Emaker to get reconstructed Tower/SMD energy
(this post: no EEMC SlowSimulator)
• Vertex z=0
• ~50K/per iteration
• Non-zero energy: 3 sigma above pedestal

Color coding:

• SVT, LOW_EM marked in legend as LowEM (single photon MC)
• STV, no-LOW_EM marked in legend as default (single photon MC)
• no-SVT, no-LOW_EM marked in legend as no-SVT (single photon MC)
• photon-jet candidates [pp2006] (used data points from this post)
• photons from eta-meson [pp2006]

Pre-shower bins:

1. Ep1 = Ep2 = 0 (no energy in both EEMC pre-shower layers)
2. Ep1 = 0, Ep2 > 0
3. 0 < Ep1 < 4 MeV
4. 4 < Ep1 < 10 MeV
5. Ep1 > 10 MeV
6. All pre-shower bins combined

Note: Ep1/Ep2 is the energy deposited in the 1st/2nd EEMC pre-shower layer.
For a single photon MC it is a sum over
all pre-shower tiles in the EEMC with energy of 3 sigma above pedestal.
For eta-meson/gamma-jet candidates from pp2006 data the sum is over 3x3 tower patch

### Shower shapes

Single particle kinematic cuts: pt=7-8GeV, eta=1.2-1.4
Eta-meson shower shapes (blue) taken from Fig. 1 from here of this post
All shapes are normalized to 1 at peak (central strip)

Figure 1: Shower shape sorted by pre-shower conditions.

Figure 2: Shower shape ratio. All shapes in Fig. 1 are divided by single photon shape
for "SVT+LOW_EM" configuration (black circles in Fig. 1)

### Sampling fraction

Figure 3: Sampling fraction (0.05 * E_reco/ E_thrown)

### Pre/post-shower energy and migration

Figure 4: Pre-shower1 energy (all tiles)

Figure 5: Pre-shower2 energy (all tiles)

Figure 6: Post-shower energy (all tiles)

Figure 7: Pre-shower bin photon migration

### Tower energy profile

Figure 8a: Energy ratio in 2x1 to 3x3 cluster
For the first 4 pre-shower bins total yield in MC is normalized to that of the data
Blue circles indicate photon-jet candidates [pp2006] (points from this post)
Same data on a linear scale

Figure 9: Average energy ratio in 2x1 to 3x3 cluster vs. thrown energy

Figure 10: Average energy ratio in 2x1 to 3x3 cluster vs. thrown energy

### LOW_EM cut definition

LOW_EM option for the STAR geometry (Low cuts on Electro-Magnetic processes)
is equivalent to the following set of GEANT cuts:

• CUTGAM=0.00001
• CUTELE=0.00001
• BCUTE =0.00001
• BCUTM =0.00001
• DCUTE =0.00001
• DCUTM =0.00001

All these values are for kinetic energy in GeV.

Cut meaning and GEANT default values:

• CUTGAM threshold for gamma transport (0.001);
• CUTELE threshold for electron and positron transport (0.001);
• BCUTE threshold for photons produced by electron bremsstrahlung (-1,);
• BCUTM threshold for photons produced by muon bremsstrahlung (-1);
• DCUTE threshold for electrons produced by electron delta-rays (-1);
• DCUTM threshold for electrons produced by muon or hadron delta-rays (-1);

Some details can be found at this link and in the GEANT manual

# 2009.10.02 Jason vs. CVS EEMC geometry: sampling fraction and shower shapes

### Tests with Jason geometry file (ecalgeo.g23)

Monte-Carlo setup:

• One photon per event
• EEMC only geometry with LOW_EM option
• Throw particles flat in eta (1.08, 2.0), phi (0, 2pi), and pt (6-10 GeV)
• Using A2Emaker to get reconstructed Tower/SMD energy
(no EEMC SlowSimulator in chain)
• Vertex z=0
• ~50K/per particle type
• Non-zero energy: 3 sigma above pedestal

Color coding:

• Photon with Jason geometry (single particle MC)
• Photon with CVS (cAir fix) geometry (single particle MC)
• Eta-meson [pp2006 data] (single photons from eta-meson decay)

### Sampling fraction

Figure 1: Sampling fraction vs. thrown energy (upper plot)
and vs. azimuthal angle (lower left) and rapidity (lower right)

### Shower shapes

Single particle kinematic cuts: pt=7-8GeV, eta=1.2-1.4
Eta-meson shower shapes (blue) taken from Fig. 1 from here of this post
All shapes are normalized to 1 at peak (central strip)

Figure 2: Shower shapes

### Shower shapes sorted by pre-shower energy

Pre-shower bins:

1. Ep1 = 0, Ep2 = 0 (no energy in both EEMC pre-shower layers)
2. Ep1 = 0, Ep2 > 0
3. 0 < Ep1 < 4 MeV
4. 4 < Ep1 < 10 MeV
5. Ep1 > 10 MeV
6. All pre-shower bins combined

Ep1/Ep2 is the energy deposited in the 1st/2nd EEMC pre-shower layer.
For a single particle MC it is a sum over
all pre-shower tiles in the EEMC with energy of 3 sigma above pedestal.
For eta-meson from pp2006 data the sum is over 3x3 tower patch

Figure 3: Shower shapes (left) and their ratio (right)

Figure 4: Shower shape ratios

# 2009.10.05 Fix to the Jason geometry file

### Why volume numbers has changed in Jason geometry file?

The number of nested volumes (nv),
is the total number of parent volumes for the sensitive volume
(sensitive volume is indicated by the HITS in the tree structure below).

For the Jason and CVS files this nv number seems to be the same
(see block tree structures below).
Then why volume ids id in g2t tables has changed?

The answer I found (which seems trivial to me know)
is that in the original (CVS) file ECAL
block has been instantiated (positioned) twice.
The second appearance is the prototype (East) version of the Endcap
(Original ecalgeo.g from CVS)

        if (emcg_OnOff==1 | emcg_OnOff==3) then
Position ECAL in CAVE z=+center
endif
if (emcg_OnOff==2 | emcg_OnOff==3) then
Position ECAL in CAVE z=-center ThetaZ=180
endif


In Jason version the second appearance has been removed
(what seems natural and it should not have any effect)
(ecalgeo.g Jason edits, g23):

        IF (emcg_OnOff>0) THEN
Create ECAL

.....

IF (emcg_OnOff==2 ) THEN
Prin1
('East Endcap has been removed from the geometry' )
ENDIF               EndIF! emcg_OnOff


Unfortunately, this affects the way GEANT counts nested volumes

(effectively the total number was reduced by 1, from 8 to 7)

and this is the reason why the volume numbering scheme

in g2t tables has been affected.

I propose to put back these East Endcap line back,

since in this case it  will not require any additional

changes to the EEMC decoder and g2t tables.

### Block tree of the geometry file

blue - added volumes in Jason file
red - G10 volume removed in Jason file
HITS - sensitive volumes

---- Jason file ----

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

### Jason geometry file

Create ECAL

Block ECAL is one EMC EndCap wheel
Create and Position EAGA AlphaZ=halfi
EndBlock

Block EAGA IS HALF OF WHEEL AIR VOLUME FORTHE ENDCAP MODULE
Create AND Position EMSS konly='MANY'
Create AND Position ECGH alphaz=90 kOnly='ONLY'
EndBlock

Block EMSS is the steel support of the endcap module
Create AND Position EFLP z=zslice-center+zwidth/2
Create AND Position ECVO z=zslice-center+zwidth/2
Create AND Position ESHM z=zslice-center+zwidth/2 kOnly='MANY'
Create AND Position ECVO z=zslice-center+zwidth/2
Create AND Position ESSP z=zslice-center+zwidth/2
Create ERCM
Create EPSB
EndBlock

Block ECVO is one of endcap volume with megatiles and radiators
Create AND Position EMOD alphaz=d3 ncopy=i_sector
EndBlock

Block ESHM is the shower maxsection
Create and Position ESPL z=currentk Only='MANY'
Create ERSM
EndBlock

Block ECGH is air gap between endcap half wheels
Create ECHC
EndBlock

Block ECHC is steel endcap half cover
EndBlock

Block ESSP is stainless steelback plate
EndBlock

Block EPSB IS A PROJECTILE STAINLESS STEEL BAR
EndBlock

Block ERCM is stainless steel tie rod in calorimeter sections
EndBlock

Block ERSM is stainless steel tie rod in shower max
EndBlock

Block EMOD (fsect,lsect) IS ONE MODULEOF THE EM ENDCAP
Create AND Position ESEC z=section-curr+secwid/2
EndBlock

Block ESEC is a single em section
Create AND Position ERAD z=length+(cell)/2+esec_deltaz
Create AND Position EMGT z=length+(gap+cell)/2+esec_deltaz
Create AND Position ERAD z=length+cell/2+esec_deltaz
EndBlock

Block EMGT is a 30 degree megatile
Create AND Position EPER alphaz=myPhi
EndBlock

Block EPER is a 5 degree slice of a 30 degree megatile (subsector)
Create and Position ETAR x=(rbot+rtop)/2ort=yzx
EndBlock

Block ETAR is a single calorimeter cell, containing scintillator, fiber router, etc...
Create AND Position EALP y=(-megatile+emcs_alincell)/2
Create AND Position ESCI y=(-megatile+g10)/2+emcs_alincell _
EndBlock

Block ESCI is the active scintillator (polystyrene) layer
EndBlock

Create AND Position ELED
EndBlock

Block ELED is a lead absorber plate
EndBlock

Block EFLP is the aluminum (aluminium) front plate of the endcap
EndBlock

Block EALP is the thin aluminium plate in calorimeter cell
EndBlock

Block ESPL is the logical volume containing an SMD plane
Create and Position EXSG alphaz=d3 ncopy=isec kOnly='MANY'
Create and Position EXSG alphaz=d3 ort=x-y-z ncopy=isec kOnly='MANY'
Create and Position EXSG alphaz=d3 ncopy=isec kOnly='MANY'
Create and Position EXSG alphaz=d3 ort=x-y-z ncopy=isec kOnly='MANY'
Create and Position EXSG alphaz=d3 ncopy=isec kOnly='MANY'
EndBlock

Block EXSG Is another logical volume... this one acutally creates the planes
Create and Position EXPS kONLY='MANY'
Create and Position EHMS x=xc y=yc alphaz=-45 kOnly='ONLY'
Create and Position EBLS x=xc y=yc z=(+esmd_apex/2+esmd_back_layer/2) alphaz=-45 kOnly='ONLY'
Create and Position EHMS x=xc y=yc alphaz=-45 ort=x-y-z kOnly='ONLY'
Create and Position EFLS x=xc y=yc z=(-esmd_apex/2-esmd_front_layer/2) alphaz=-45 ort=x-y-z kOnly='ONLY'
EndBlock

Block EHMS defines the triangular SMD strips
Endblock! EHMS

Block EFLS is the layer of material on the front of the SMD planes
EndBlock! EFLS

Block EBLS is the layer of material on the back of the SMD planes
EndBlock! EFLS

Block EXPS is the plastic spacer in the shower maximum section
EndBlock



### CVS geometry file

Create ECAL

Block ECAL is one EMC EndCap wheel
Create and Position EAGA AlphaZ=halfi
EndBlock

Block EAGA is half of wheel air volume forthe EndCap module
Create and Position EMSS konly='MANY'
Create and Position ECGH AlphaZ=90 konly='ONLY'
EndBlock

Block EMSS is steel support of the EndCap module
Create and Position EFLP z=zslice-center+slcwid/2
Create and Position ECVO z=zslice-center+slcwid/2
Create and Position ESHM z=zslice-center+slcwid/2
Create and Position ECVO z=zslice-center+slcwid/2
Create and Position ESSP z=zslice-center+slcwid/2
Create ERCM
Create EPSB
EndBlock

Block ECVO is one of EndCap Volume with megatiles and radiators
Create and Position EMOD AlphaZ=d3 Ncopy=J_section
EndBlock

Block ESHM is the SHower Maxsection
Create and Position ESPL z=current
Create ERSM
Endblock

Block ECGH is air Gap between endcap Half wheels
Create ECHC
EndBlock

Block ECHC is steel EndCap Half Cover
EndBlock

Block ESSP is Stainless Steelback Plate
endblock

Block EPSB is Projectile Stainless steel Bar
endblock

Block ERCM is stainless steel tie Rod in CaloriMeter sections
endblock

Block ERSM is stainless steel tie Rod in Shower Max
endblock

Block EMOD is one moduleof the EM EndCap
Create and Position ESEC z=section-curr+secwid/2
endblock

Block ESEC is a single EM section
Create and Position ERAD z=len + (cell)/2
Create and Position EMGT z=len +(gap+cell)/2
Create and Position ERAD z=len + cell/2
Endblock

Block EMGT is a megatile EM section
Create and Position EPER AlphaZ=(emcs_Nslices/2-isec+0.5)*dphi
Endblock

Block EPER is a EM subsection period (super layer)
Create and Position ETAR x=(RBot+RTop)/2ORT=YZX
EndBlock

Block ETAR is one CELL of scintillator, fiber and plastic
Create and Position EALP y=(-mgt+emcs_AlinCell)/2
Create and Position ESCI y=(-mgt+G10)/2+emcs_AlinCell _
EndBlock

Block ESCI is the active scintillator (polystyren) layer
endblock

Create and PositionELED
endblock

Block ELED is lead absorber Plate
endblock

Block EFLP is First Aluminium plate
endblock

Block EALP is ALuminiumPlate in calorimeter cell
endblock

Block ESPL is one of the Shower maxPLanes
Create and position EXSG AlphaZ=d3Ncopy=isec
Create and position EXSG AlphaZ=d3Ncopy=isec
Create and position EXGT z=msecwd AlphaZ=d3
Create and position EXSG AlphaZ=d3 ORT=X-Y-Z Ncopy=isec
Create and position EXGT z=-msecwd AlphaZ=d3
Create and position EXSG AlphaZ=d3Ncopy=isec
Create and position EXGT z=msecwd AlphaZ=d3
Create and position EXSG AlphaZ=d3 ORT=X-Y-Z Ncopy=isec
Create and position EXGT z=-msecwd AlphaZ=d3
Endblock

Block EXSG is the Shower maxGap for scintillator strips
Create EHMS
endblock

Block EHMS is sHower Max Strip
Endblock

Block EXGT is the G10 layer in the Shower Max
EndBlock



# Original (ecalgeo.g) file from CVS

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,
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,

*
Real       maxcnt,msecwd,mxgten,curr,Secwid,Section,
curcl,EtaTop,EtaBot,slcwid,zslice,Gap,mgt,
xleft,xright,yleft,yright,current,
Rdel,dxy,ddn,ddup

Integer    N
Parameter (N=12)
*
Tanf(etax) = tan(2*atan(exp(-etax)))
*
* ----------------------------------------------------------------------------
*
* FillMode =1 only 2-5 sectors (in the first half) filled with scintillators
* FillMode =2 all sectors filled (still only one half of one side)
* FillMode =3 both halves (ie all 12 sectors are filled)

Fill  EMCG                          ! EM EndCAp Calorimeter basic data
Version  = 5.0                ! Geometry version
OnOff    = 3                  ! Configurations 0-no, 1-west 2-east 3-both
FillMode = 3                  ! sectors fill mode

Fill  EMCS                          ! EM Endcap Calorimeter geometry
Type     = 1                  ! =1 endcap, =2 fpd edcap prototype
ZOrig    = 268.763            ! calorimeter origin in z
ZEnd     = 310.007            ! Calorimeter end in z
EtaMin   = 1.086              ! upper feducial eta cut
EtaMax   = 2.0,               ! lower feducial eta cut
PhiMin   = -90                ! Min phi
PhiMax   = 90                 ! Max phi
Offset   = 0.0                ! offset in x
Nsupsec  = 6                  ! Number of azimuthal supersectors
Nsector  = 30                 ! Number of azimutal sectors (Phi granularity)
Nslices  = 5                  ! number of phi slices in supersector
Nsection = 4                  ! Number of readout sections
Front    = 0.953              ! thickness of the front AL plates
AlinCell   = 0.02             ! Aluminim plate in cell
Frplast  = 0.015              ! Front plastic in megatile
Bkplast  = 0.155              ! Fiber routing guides and back plastic
Pbplate  = 0.457              ! Lead radiator thickness
LamPlate  = 0.05              ! Laminated SS plate thickness
BckPlate = 3.175              ! Back SS plate thickness
Hub      = 3.81               ! thickness of EndCap hub
Rmshift  = 2.121              ! radial shift of module
smshift  = 0.12               ! radial shift of steel support walls
GapPlt   = 0.3/2              ! HALF of the inter-plate gap in phi
GapCel   = 0.03/2             ! HALF of the radial inter-cell gap
GapSMD   = 3.400              ! space for SMD detector
SMDcentr = 279.542            ! SMD position
TieRod   = {160.,195}         ! Radial position of tie rods
Bckfrnt  = 306.832            ! Backplate front Z
GapHalf  = 0.4                ! 1/2 Gap between halves of endcap wheel
Cover    = 0.075              ! Cover of wheel half
*      Rmshift  = 2.121              ! radial shift of module
* --------------------------------------------------------------------------
Fill EETR                      ! Eta and Phi grid values
Type     = 1             ! =1 endcap, =2 fpd
EtaGr    = 1.0536        ! eta_top/eta_bot tower granularity
PhiGr    = 0.0981747     ! Phi granularity (radians)
NEta     = 12            ! Eta granularity
EtaBin   = {2.0,1.9008,1.8065,1.7168,1.6317,1.5507,1.4738,
1.4007,1.3312,1.2651,1.2023,1.1427,1.086}! Eta rapidities
*---------------------------------------------------------------------------
Fill ESEC           ! First EM section
ISect    = 1                           ! Section number
Nlayer   = 1                           ! Number of Sci layers along z
Cell     = 1.505                       ! Cell full width in z
Scint    = 0.5                         ! Sci layer thickness
*
Fill ESEC           ! First EM section
ISect    = 2                           ! Section number
Nlayer   = 1                           ! Number of Sci layers along z
Cell     = 1.505                       ! Cell full width in z
Scint    = 0.5                         ! Sci layer thickness
*
Fill ESEC           ! Second EM section
ISect    = 3                           ! Section number
Nlayer   = 4                           ! Number of Sci layers along z
Cell     = 1.405                       ! Cell full width in z
Scint    = 0.4                         ! Sci layer thickness
*
Fill ESEC           ! Third EM section
ISect    = 4                           ! Section
Nlayer   = 18                          ! Number of layers along z
Cell     = 1.405                       ! Cell full width in z
Scint    = 0.4                         ! Sci layer thickness
*
Fill ESEC           ! 4th EM section
ISect    = 5                           ! Section
Nlayer   = 1                           ! Number of  layers along z
Cell     = 1.505                       ! Cell full width in z
Scint    = 0.5                         ! Sci layer thickness
*----------------------------------------------------------------------------
Fill EMXG           ! EM Endcap SMD basic data
Version   = 1                         ! Geometry version
Sapex     = 0.7                       ! Scintillator strip apex
Sbase     = 1.0                       ! Scintillator strip base
Rin = 77.41                           ! inner radius of SMD plane
Rout = 213.922                        ! outer radius of SMD plane
F4 = .15                              ! F4 thickness
*----------------------------------------------------------------------------
Fill EXSE           ! First SMD section
JSect    = 1                           ! Section number
Zshift   = -1.215                      ! Section width
sectype  = {4,1,0,2,1,0}               ! 1-V,2-U,3-cutV,4-cutU
*
Fill EXSE           ! Second SMD section
JSect    = 2                           ! Section number
Zshift   = 0.                          ! Section width
sectype  = {0,2,1,0,2,3}               ! 1-V,2-U,3-cutV,4-cutU
*
Fill EXSE           ! Third SMD section
JSect    = 3                           ! Section number
Zshift   = 1.215                       ! Section width
sectype  = {1,0,2,1,0,2}               ! 1-V,2-U,3-cutV,4-cutU

*----------------------------------------------------------------------------
*
Use    EMCG
*
sq3 = sqrt(3.)
sq2 = sqrt(2.)

prin1 emcg_version
('ECALGEO version ', F4.2)

* Endcap
USE EMCS type=1
USE EETR type=1
orgkeep =  emcs_ZOrig
endkeep =  emcs_ZEnd
if(emcg_OnOff>0) then
diff = 0.0
center  = (emcs_ZOrig+emcs_ZEnd)/2
Tan_Upp = tanf(emcs_EtaMin)
Tan_Low = tanf(emcs_EtaMax)
rth  = sqrt(1. + Tan_Low*Tan_Low)
rshift  = emcs_Hub * rth
dup=emcs_Rmshift*Tan_Upp
dd=emcs_Rmshift*rth
d2=rshift + dd
radiator  = emcs_Pbplate + 2*emcs_LamPlate
*       d3=emcs_Rmshift-2*emcs_smshift
dphi = (emcs_PhiMax-emcs_PhiMin)/emcs_Nsector
Create ECAL
if (emcg_OnOff==1 | emcg_OnOff==3) then
Position ECAL in CAVE z=+center
endif
if (emcg_OnOff==2 | emcg_OnOff==3) then
Position ECAL in CAVE z=-center ThetaZ=180
endif

if(section > emcs_Zend) then
prin0 section,emcs_Zend
(' ECALGEO error: sum of sections exceeds maximum ',2F12.4)
endif
prin1 section
(' EndCap calorimeter total depth ',F12.4)
endif

prin1
('ECALGEO finished')
*
* ----------------------------------------------------------------------------
Block ECAL is one EMC EndCap wheel
Material  Air
Medium    standard
Attribute ECAL   seen=1 colo=7                            !  lightblue
shape     CONE   dz=(emcs_Zend-emcs_ZOrig)/2,
Rmn1=orgkeep*Tan_Low-d2 Rmn2=endkeep*Tan_Low-d2,
Rmx1=orgkeep*Tan_Upp+dup Rmx2=endkeep*Tan_Upp+dup

do ihalf=1,2
filled=1
halfi = -105 + (ihalf-1)*180
if (ihalf=2 & emcg_FillMode<3) filled = 0

Create and Position EAGA  AlphaZ=halfi

enddo
*

EndBlock
* ----------------------------------------------------------------------------
Block EAGA is half of wheel air volume for  the EndCap module
Attribute EAGA      seen=1    colo=1   serial=filled           ! black

Material  Air
shape     CONS   dz=(emcs_Zend-emcs_ZOrig)/2,
Rmn1=orgkeep*Tan_Low-d2 Rmn2=endkeep*Tan_Low-d2,
Rmx1=orgkeep*Tan_Upp+dup Rmx2=endkeep*Tan_Upp+dup,
phi1=emcs_PhiMin phi2=emcs_PhiMax

if (filled=1) then
Create and Position EMSS  konly='MANY'
curr = orgkeep ; curcl = endkeep
Create and position ECGH  AlphaZ=90 konly='ONLY'
endif

EndBlock

* ----------------------------------------------------------------------------
Block EMSS is steel support of the EndCap module
Attribute EMSS      seen=1    colo=1              ! black

Material  Iron
shape     CONS   dz=(emcs_Zend-emcs_ZOrig)/2,
Rmn1=orgkeep*Tan_Low-d2 Rmn2=endkeep*Tan_Low-d2,
Rmx1=orgkeep*Tan_Upp+dup Rmx2=endkeep*Tan_Upp+dup,
phi1=emcs_PhiMin phi2=emcs_PhiMax

zslice = emcs_ZOrig
prin1 zslice
(' Front Al plane starts at:  ',F12.4)
slcwid  = emcs_Front
Create and Position EFLP  z=zslice-center+slcwid/2
zslice = zslice + slcwid

prin1 zslice
(' First calorimeter starts at:  ',F12.4)

fsect = 1; lsect = 3

slcwid = emcs_SMDcentr - emcs_GapSMD/2 - zslice
*
Create and Position ECVO  z=zslice-center+slcwid/2

slcwid  = emcs_GapSMD
zslice = emcs_SMDcentr - emcs_GapSMD/2

prin1 section,zslice
(' 1st calorimeter ends, SMD starts at:  ',2F10.5)

Create and Position ESHM  z=zslice-center+slcwid/2
zslice = zslice + slcwid

prin1 zslice
('  SMD ends at:  ',F10.5)
*
slcwid = 0
fsect = 4; lsect = 5
do I_section =fsect,lsect
USE ESEC Isect=I_section
Slcwid  = slcwid + esec_cell*esec_Nlayer
enddo

slcwid = emcs_bckfrnt - zslice

*
Create and Position ECVO  z = zslice-center+slcwid/2

zslice = emcs_bckfrnt

prin1 section,zslice
(' 2nd calorimeter ends, Back plate starts at:  ',2F10.5)

slcwid  = emcs_BckPlate
*
Create and Position ESSP    z=zslice-center+slcwid/2
zslice = zslice + slcwid
prin1 zslice
(' BackPlate ends at:  ',F10.5)

slcwid = emcs_Zend-emcs_ZOrig
Create ERCM

do i_str = 1,2
do is = 1,5
xx = emcs_phimin + is*30
xc = cos(yy)*emcs_TieRod(i_str)
yc = sin(yy)*emcs_TieRod(i_str)
Position ERCM z=0 x=xc y=yc
enddo
enddo

rth = orgkeep*Tan_Upp+dup + 2.5/2
xc = (endkeep - orgkeep)*Tan_Upp
len = .5*(endkeep + orgkeep)*Tan_Upp + dup + 2.5/2
yc = emcs_Zend-emcs_ZOrig

Create EPSB
do is = 1,6
xx = -75 + (is-1)*30
xc = cos(yy)*len
yc = sin(yy)*len
Position EPSB x=xc y=yc  AlphaZ=xx
enddo

EndBlock
* ----------------------------------------------------------------------------
Block ECVO is one of EndCap Volume with megatiles and radiators
Material  Air
Attribute ECVO   seen=1 colo=3                            ! green
shape     CONS   dz=slcwid/2,
Rmn1=zslice*Tan_Low-dd Rmn2=(zslice+slcwid)*Tan_Low-dd,
Rmx1=zslice*Tan_Upp+dup Rmx2=(zslice+slcwid)*Tan_Upp+dup

do J_section = 1,6
if (1 < J_section < 6 | emcg_FillMode > 1)then
filled = 1
else
filled = 0
endif
d3 = 75 - (J_section-1)*30
Create and Position EMOD AlphaZ=d3   Ncopy=J_section
enddo

*

EndBlock
* ----------------------------------------------------------------------------
Block ESHM  is the SHower Max  section
*
Material  Air
Attribute ESHM   seen=1   colo=4                  !  blue
Shape     CONS   dz=SlcWid/2,
rmn1=zslice*Tan_Low-dd,
rmn2=(zslice+slcwid)*Tan_Low-dd,
rmx1=(zslice)*Tan_Upp+dup,
rmx2=(zslice+slcwid)*Tan_Upp+dup,
phi1=emcs_PhiMin phi2=emcs_PhiMax

USE EMXG Version=1
maxcnt = emcs_SMDcentr
prin1 zslice,section,center
(' Z start for SMD,section:  ',3F12.4)
*
do J_section = 1,3
USE EXSE Jsect=J_section
*
current = exse_Zshift
secwid  = emxg_Sapex + 2.*emxg_F4
section = maxcnt + exse_zshift
prin1 j_section,current,section,secwid
(' layer, Z, width :  ',i3,3F12.4)
rbot=section*Tan_Low
rtop=section*Tan_Upp
prin1 j_section,rbot,rtop
(' layer, rbot,rtop :  ',i3,2F12.4)
Create and position ESPL z=current
*
end do

Create ERSM
do i_str = 1,2
do is = 1,5
xx = emcs_phimin + (is)*30
xc = cos(yy)*emcs_TieRod(i_str)
yc = sin(yy)*emcs_TieRod(i_str)
Position ERSM z=0 x=xc y=yc
enddo
enddo

Endblock
* ----------------------------------------------------------------------------
Block ECGH is air Gap between endcap Half wheels
Material  Air
Medium    standard
Attribute ECGH   seen=0 colo=7                            !  lightblue
shape     TRD1   dz=(emcs_Zend-emcs_ZOrig)/2,
dy =(emcs_gaphalf+emcs_cover)/2,
dx1=orgkeep*Tan_Upp+dup,
dx2=endkeep*Tan_Upp+dup

rth = emcs_GapHalf + emcs_cover
xx=curr*Tan_Low-d2
xleft = sqrt(xx*xx - rth*rth)
yy=curr*Tan_Upp+dup
xright = sqrt(yy*yy - rth*rth)
secwid = yy - xx
xx=curcl*Tan_Low-d2
yleft = sqrt(xx*xx - rth*rth)
yy=curcl*Tan_Upp+dup
yright = sqrt(yy*yy - rth*rth)
slcwid = yy - xx
xx=(xleft+xright)/2
yy=(yleft + yright)/2
xc = yy - xx
len = (xx+yy)/2
yc = curcl - curr
rth = -(emcs_GapHalf + emcs_cover)/2
Create  ECHC
Position ECHC  x=len y=rth
Position ECHC  x=-len y=rth AlphaZ=180

EndBlock
* ----------------------------------------------------------------------------
Block ECHC is steel EndCap Half Cover
Attribute ECHC      seen=1    colo=1              ! black

Material  Iron
shape     TRAP   dz=(curcl-curr)/2,
thet=p,
bl1=secwid/2,
tl1=secwid/2,
bl2=slcwid/2,
tl2=slcwid/2,
h1=emcs_cover/2 h2=emcs_cover/2,
phi=0  alp1=0 alp2=0
EndBlock
* ----------------------------------------------------------------------------
Block ESSP  is Stainless Steel  back Plate
*
Material  Iron
Attribute ESSP   seen=1  colo=6 fill=1
shape     CONS   dz=emcs_BckPlate/2,
Rmn1=zslice*Tan_Low-dd Rmn2=(zslice+slcwid)*Tan_Low-dd,
Rmx1=zslice*Tan_Upp+dup Rmx2=(zslice+slcwid)*Tan_Upp+dup,
phi1=emcs_PhiMin phi2=emcs_PhiMax
endblock
* ----------------------------------------------------------------------------
Block EPSB  is Projectile Stainless steel Bar
*
Material  Iron
Attribute EPSB   seen=1  colo=6 fill=1
shape     TRAP   dz=(emcs_Zend-emcs_ZOrig)/2,
thet=p,
bl1=2.5/2,
tl1=2.5/2,
bl2=2.5/2,
tl2=2.5/2,
h1=2.0/2  h2=2.0/2,
phi=0  alp1=0 alp2=0
endblock
* ----------------------------------------------------------------------------
Block ERCM  is stainless steel tie Rod in CaloriMeter sections
*
Material  Iron
Attribute ERSM   seen=1  colo=6 fill=1
shape     TUBE   dz=slcwid/2,
rmin=0,
rmax=1.0425  !    nobody knows exactly
endblock
* ----------------------------------------------------------------------------
Block ERSM  is stainless steel tie Rod in Shower Max
*
Material  Iron
Attribute ERSM   seen=1  colo=6 fill=1
shape     TUBE   dz=slcwid/2,
rmin=0,
rmax=1.0425
endblock
* ----------------------------------------------------------------------------
Block EMOD is one module  of the EM EndCap
Attribute EMOD      seen=1    colo=3  serial=filled         ! green
Material  Air
Shape     CONS   dz=slcwid/2,
phi1=emcs_PhiMin/emcs_Nsupsec,
phi2=emcs_PhiMax/emcs_Nsupsec,
Rmn1=zslice*Tan_Low-dd  Rmn2=(zslice+slcwid)*Tan_Low-dd,
Rmx1=zslice*Tan_Upp+dup Rmx2=(zslice+slcwid)*Tan_Upp+dup
*
*    Running parameter 'section' contains the position of the current section
*     It should not be modified in daughters, use 'current' variable instead.
*     SecWid is used in all 'CONS' daughters to define dimensions.
*
*
section = zslice
curr = zslice + slcwid/2

Do I_section =fsect,lsect

USE ESEC Isect=I_section
*
Secwid  = esec_cell*esec_Nlayer
if (I_section = 3 | I_section = 5) then   ! no last radiator
Secwid  = Secwid - radiator
else if (I_section = 4) then         ! add one more radiator
Secwid  = Secwid - esec_cell + radiator
endif
Create and position ESEC      z=section-curr+secwid/2
section = section + secwid
*
enddo
endblock
* ----------------------------------------------------------------------------
Block ESEC is a single EM section
Attribute ESEC   seen=1    colo=1 serial=filled
Material Air
Medium standard
*
Shape     CONS  dz=secwid/2,
rmn1=(section-diff)*Tan_Low-dd,
rmn2=(section+secwid-diff)*Tan_Low-dd,
rmx1=(section-diff)*Tan_Upp+dup,
rmx2=(section+secwid-diff)*Tan_Upp+dup
*
len = -secwid/2
current = section
mgt = esec_scint + emcs_AlinCell _
+ emcs_FrPlast + emcs_BkPlast
gap = esec_cell - radiator - mgt
prin2 I_section,section
(' ESEC:I_section,section',i3,F12.4)

Do is = 1,esec_Nlayer

* define actual  cell thickness:
Cell = esec_cell
*
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
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
Create and Position    ERAD     z=len + cell/2
len = len + cell
current = current + cell
end if
end if
end do
Endblock
* ----------------------------------------------------------------------------
Block EMGT is a megatile EM section
Attribute EMGT   seen=1  colo=1
Material Air
Medium standard
*
Shape     CONS  dz=mgt/2,
rmn1=(current-diff)*Tan_Low-dd,  rmn2=(current+mgt-diff)*Tan_Low-dd,
rmx1=(current-diff)*Tan_Upp+dup, rmx2=(current+mgt-diff)*Tan_Upp+dup

if (I_section=1 | I_section=2 | I_section=5) then
Call GSTPAR (ag_imed,'CUTGAM',0.00001)
Call GSTPAR (ag_imed,'CUTELE',0.00001)
else
Call GSTPAR (ag_imed,'CUTGAM',0.00008)
Call GSTPAR (ag_imed,'CUTELE',0.001)
Call GSTPAR (ag_imed,'BCUTE',0.0001)
end if
*
Do isec=1,nint(emcs_Nslices)
Create and Position EPER AlphaZ=(emcs_Nslices/2-isec+0.5)*dphi
End Do
Endblock
*---------------------------------------------------------------------------
Block EPER  is a EM subsection period (super layer)
*
Material  POLYSTYREN
Attribute EPER   seen=1  colo=1
Shape     CONS  dz=mgt/2,
phi1=emcs_PhiMin/emcs_Nsector,
phi2=+emcs_PhiMax/emcs_Nsector,
rmn1=(current-diff)*Tan_Low-dd,
rmn2=(current+mgt-diff)*Tan_Low-dd,
rmx1=(current-diff)*Tan_Upp+dup,
rmx2=(current+mgt-diff)*Tan_Upp+dup
*
curcl = current+mgt/2
Do ie = 1,nint(eetr_NEta)
EtaBot  = eetr_EtaBin(ie)
EtaTop  = eetr_EtaBin(ie+1)

RBot=(curcl-diff)*Tanf(EtaBot)
*
if(Plate > 0) then         ! Ordinary Sci layer
RTop=min((curcl-diff)*Tanf(EtaTop), _
((current-diff)*Tan_Upp+dup))
else                     ! last Sci layer in section
RTop=min((curcl-diff)*Tanf(EtaTop), _
((current-diff)*Tan_Upp+dup))
endif
check RBot<RTop
*
xx=tan(pi*emcs_PhiMax/180.0/emcs_Nsector)
yy=cos(pi*emcs_PhiMax/180.0/emcs_Nsector)

Create and Position  ETAR    x=(RBot+RTop)/2  ORT=YZX
prin2 ie,EtaTop,EtaBot,rbot,rtop
(' EPER : ie,EtaTop,EtaBot,rbot,rtop ',i3,4F12.4)
enddo
*
EndBlock
*  - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Block ETAR is one CELL of scintillator, fiber and plastic
*
Attribute ETAR   seen=1  colo=4                           ! blue
*     local z goes along the radius, y is the thickness
Shape     TRD1   dy=mgt/2   dz=(RTop-RBot)/2,
dx1=RBot*xx-emcs_GapCel/yy,
dx2=RTop*xx-emcs_GapCel/yy
*
Create and Position EALP          y=(-mgt+emcs_AlinCell)/2
G10 = esec_scint
Create and Position    ESCI       y=(-mgt+G10)/2+emcs_AlinCell _
+emcs_FrPlast
EndBlock
* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Block ESCI  is the active scintillator (polystyren) layer
*
Material  POLYSTYREN
Material  Cpolystyren   Isvol=1
Attribute ESCI   seen=1   colo=7   fill=0         ! lightblue
*     local z goes along the radius, y is the thickness
Shape     TRD1   dy=esec_scint/2,
dz=(RTop-RBot)/2-emcs_GapCel
Call GSTPAR (ag_imed,'CUTGAM',0.00008)
Call GSTPAR (ag_imed,'CUTELE',0.001)
Call GSTPAR (ag_imed,'BCUTE',0.0001)
Call GSTPAR (ag_imed,'CUTNEU',0.001)
Call GSTPAR (ag_imed,'CUTMUO',0.001)
* define Birks law parameters
Call GSTPAR (ag_imed,'BIRK1',1.)
Call GSTPAR (ag_imed,'BIRK2',0.013)
Call GSTPAR (ag_imed,'BIRK3',9.6E-6)
*
HITS ESCI   Birk:0:(0,10)
*                  xx:16:H(-250,250)   yy:16:(-250,250)   zz:16:(-350,350),
*                  px:16:(-100,100)    py:16:(-100,100)   pz:16:(-100,100),
*                  Slen:16:(0,1.e4)    Tof:16:(0,1.e-6)   Step:16:(0,100),
*                  none:16:
endblock
* ----------------------------------------------------------------------------
*
Material  Iron
Attribute ERAD   seen=1  colo=6 fill=1            ! violet
rmn1=(current)*Tan_Low-dd,
rmn2=(current+cell)*Tan_Low-dd,
rmx1=(current)*Tan_Upp+dup,

Create and Position    ELED

endblock
* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Block ELED  is lead absorber Plate
*
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,'CUTMUO',0.001)

endblock
* ----------------------------------------------------------------------------
Block EFLP  is First Aluminium plate
*
Material  Aluminium
Attribute EFLP   seen=1  colo=3 fill=1                    ! green
shape     CONS   dz=emcs_Front/2,
Rmn1=68.813 Rmn2=68.813,
Rmx1=(zslice-diff)*Tan_Upp+dup,
Rmx2=(zslice + slcwid-diff)*Tan_Upp+dup,
phi1=emcs_PhiMin phi2=emcs_PhiMax

endblock
* ----------------------------------------------------------------------------
Block EALP  is ALuminium  Plate in calorimeter cell
*
Material  Aluminium
Material  StrAluminium isvol=0
Attribute EALP   seen=1  colo=1
Shape     TRD1   dy=emcs_AlinCell/2  dz=(RTop-RBot)/2
Call GSTPAR (ag_imed,'CUTGAM',0.00001)
Call GSTPAR (ag_imed,'CUTELE',0.00001)
Call GSTPAR (ag_imed,'LOSS',1.)
Call GSTPAR (ag_imed,'STRA',1.)