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

Pibero Djawotho – STAR – UC Davis 2 Dominant background to prompt γ production: π 0 (η)→γγ γ/π 0 ≈1/40 at p T =5 GeV to 1/10 at p T =10 GeV dN γ /dp T ~exp(-0.69p T ) from Pythia Challenge: how low in p T can analysis be reasonably carried out while retaining high efficiency and purity Heavily rely on clever software algorithms for γ/π 0 separation and specialized subdetectors: shower max and preshower

June 18, 2008Pibero Djawotho – STAR – UC Davis 3 γ/π 0 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) –π 0 →γγ  double peak structure: main peak and peaklet (asymmetric π 0 decay) –Fit main peak and compute residual=data-fit on each side of main peak  pick maximum residual –For given energy E, π 0 should have more residual than γ

June 18, 2008Pibero Djawotho – STAR – UC Davis 4 Functional form of fit function Real data (run= /ev=254105)

June 18, 2008Pibero Djawotho – STAR – UC Davis 5 Single thrown γ and π 0 10k γ/π 0 each sample STAR y2006 geometry z-vertex at 0 Flat in p T =10-30 GeV/c Flat in η= Quadratic y(x)= x 2

June 18, 2008Pibero Djawotho – STAR – UC Davis 6 Background rejection vs. signal efficiency 75% 75% rejection Use perp distance from quadratic to project in 1D Not quite the from original proposal but this simulation has most up-to-date detector configuration.

June 18, 2008Pibero Djawotho – STAR – UC Davis 7 Background rejection vs. signal efficiency We start to lose efficiency with this method at higher γ energies.

June 18, 2008Pibero Djawotho – STAR – UC Davis 8 Pythia prompt γ production in pp collisions at √s=200 GeV Pythia 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

June 18, 2008Pibero 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 π 0 →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

June 18, 2008Pibero 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!!!

June 18, 2008Pibero Djawotho – STAR – UC Davis 11 Photons from etas (η→γγ) Use standard π 0 finder with L2- gamma trigger 0.45<m γγ <0.65 GeV p T (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

June 18, 2008Pibero Djawotho – STAR – UC Davis 12 Photons from γ-jets (See Ilya’s talk) Select dijets from Run 6 Define neutral energy fraction R EM =(E T (Endcap)+ E T (Barrel))/E T (total) R EM (jet 1 )>0.9 and R EM (jet 2 )<0.9 Number of tracks(jet 1 )<2 cos(φ 1 -φ 2 )<-0.9  “back-to-back” jets 0<number of Endcap towers<3

June 18, 2008Pibero 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)

June 18, 2008Pibero 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

June 18, 2008Pibero 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

EXTRA SLIDES

June 18, 2008Pibero Djawotho – STAR – UC Davis 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 5X SMD strips/plane/sector