Photon-jet reconstruction with the EEMC – Deuxième Partie

1. STAR Photon-jet reconstruction with the EEMC – Deuxième Partie Pibero Djawotho Indiana University Cyclotron Facility June 18, 2008

2. Dominant background to prompt γ production:π0(η)→γγ • γ/π0≈1/40 at pT=5 GeV to 1/10 at pT=10 GeV • dNγ/dpT~exp(-0.69pT) from Pythia 6.406 • Challenge: how low in pT can analysis be reasonably carried out while retaining high efficiency and purity • Heavily rely on clever software algorithms for γ/π0 separation and specialized subdetectors: shower max and preshower Pibero Djawotho – STAR – UC Davis

3. γ/π0 discrimination in Endcap SMD: Maximum Sided Residual • Basic idea: • Look at transverse shower profile in the SMD • γ and e transverse shower profile  single peak  narrow Gaussian+wide Gaussian with common centroid in each SMD plane (u and v) • π0→γγ double peak structure: main peak and peaklet (asymmetric π0 decay) • Fit main peak and compute residual=data-fit on each side of main peak  pick maximum residual • For given energy E, π0 should have more residual than γ Pibero Djawotho – STAR – UC Davis

4. Functional form of fit function Real data (run=7155062/ev=254105) Pibero Djawotho – STAR – UC Davis

5. Single thrown γ and π0 • 10k γ/π0 each sample • STAR y2006 geometry • z-vertex at 0 • Flat in pT=10-30 GeV/c • Flat in η=1.0-2.1 Quadratic y(x)=100+0.1x2 Pibero Djawotho – STAR – UC Davis

6. 75% eff @ 75% rejection Background rejection vs. signal efficiency Use perp distance from quadratic to project in 1D Not quite the 80-80 from original proposal but this simulation has most up-to-date detector configuration. Pibero Djawotho – STAR – UC Davis

7. Background rejection vs. signal efficiency We start to lose efficiency with this method at higher γenergies. Pibero Djawotho – STAR – UC Davis

8. Pythia 6.406 prompt γ production in pp collisions at √s=200 GeV • Pythia 6.406 prompt → production subprocesses: • q+qbar → q+γ (10% contribution) • f+fbar → γ+γ • q+g → q+γ (qg Compton scattering  dominant subprocess) • g+g → γ+γ • g+g → g+γ Pibero Djawotho – STAR – UC Davis

9. How realistic is simulation of SMD response? • All shower shapes are normalized to unit area • MC photons are default GEANT+STAR simulation response • Will’s photons are selected from η-region of a π0→γγ finder on Run 6 data • Pibero’s photons are from simple η→γγ finder with soft isolation in SMD and no EMC clustering on Run 6 data • Conclusion: • Simulation does not accurately reproduce data • MC shower shapes and RMS are narrower Pibero Djawotho – STAR – UC Davis

10. How to make MC more realistic • Compile library of shower shapes from data • In MC, replace all γ shower shapes (25 strips) with shapes from library after proper energy scaling, translation in SMD plane and superposition on underlying event  Data-driven MC • Library shapes are binned by: • SMD plane (U and V) • Sector configuration (plane ordering) • Photon energy • Preshower energy Consistency check: data-driven MC agrees with data!!! Pibero Djawotho – STAR – UC Davis

11. Photons from etas (η→γγ) • Use standard π0 finder with L2-gamma trigger • 0.45<mγγ<0.65 GeV • pT(η)>6 GeV • Turn off splitting algorithm • 5 MeV seed threshold • No floors • No dead strips • Minimum 20-strip separation between clusters • Energy sum of middle 5 strips over 20 strips>70%  soft SMD isolation cut • Require 2 points/plane S/B better than 1:1 Pibero Djawotho – STAR – UC Davis

12. Photons from γ-jets (See Ilya’s talk) • Select dijets from Run 6 • Define neutral energy fraction REM=(ET(Endcap)+ ET(Barrel))/ET(total) • REM(jet1)>0.9 and REM(jet2)<0.9 • Number of tracks(jet1)<2 • cos(φ1-φ2)<-0.9  “back-to-back” jets • 0<number of Endcap towers<3 Pibero Djawotho – STAR – UC Davis

13. Shower Shapes • All shower shapes normalized to unit area • MC shower shape is narrower • 3-Gaussian better describes the data (esp. tails) • All data shower shapes are consistent (γ’s from η’s and γ’s from γ-jets) Pibero Djawotho – STAR – UC Davis

14. Maximum sided residual revisited • Generate prompt γ with Pythia • Generate QCD background with Pythia • Run through GEANT+STAR reconstruction chain • Replace all MC γ shower shapes with data shapes from library in appropriate bins • Apply maximum sided residual cut  background rejection vs. signal efficiency Pibero Djawotho – STAR – UC Davis

15. Conclusion and Outlook • γ-jets offer clean probe to ΔG at RHIC by predominantly sampling qg-Compton channel • Very good agreement between MC and data with preshower1=preshower2=0  Can achieve 1:1 signal-to-background ratio before any SMD cut • Ongoing studies to understand discrepancies between MC and data shower shapes with preshower1>0 and preshower2>0 • Analysis of Run 8 data (SVT and support structures removed) once produced will provide crucial information on amount of material (conversion) before the calorimeter Pibero Djawotho – STAR – UC Davis

16. EXTRA SLIDES

17. STAR Endcap Electromagnetic Calorimeter • Coverage: 1.086<η<2.0, 0<φ<2π • 12 sectors×5 subsectors×η-bins=720 towers • 1 tower=24 layers: • Layer 1=preshower-1 • Layer 2=preshower-2 • Layer-24=postshower • SMD-u and –v plane at 5X0 • 288 SMD strips/plane/sector Pibero Djawotho – STAR – UC Davis