photon-jet reconstruction with the eemc – deuxième partie pibero djawotho indiana university...
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Photon-jet reconstruction with the EEMC – Deuxième
PartiePibero Djawotho
Indiana University Cyclotron Facility
June 18, 2008
STASTARR
June 18, 2008 Pibero Djawotho – STAR – UC Davis 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
June 18, 2008 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
June 18, 2008 Pibero Djawotho – STAR – UC Davis 4
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Functional form of fit function
12 5.3
Real data (run=7155062/ev=254105)
June 18, 2008 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
June 18, 2008 Pibero Djawotho – STAR – UC Davis 6
Background rejection vs. signal efficiency
75% eff @75% rejection
Use perp distancefrom quadratic toproject in 1D
Not quite the 80-80from original proposalbut this simulation hasmost up-to-date detectorconfiguration.
June 18, 2008 Pibero Djawotho – STAR – UC Davis 7
Background rejection vs. signal efficiency
We start to loseefficiency withthis method athigher γ energies.
June 18, 2008 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
June 18, 2008 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
Comparison of Shower Shapes
0.001
0.010
0.100
1.000
0 5 10 15 20 25
Strip Number
He
igh
t Will's photons
MC photons
Pibero's photons
Comparison of RMS values
0.000
0.020
0.040
0.060
0.080
0.100
0.120
0.140
0 5 10 15 20 25
Strip Number
RM
S u
nc
ert
ain
ty
Will's photons
MC photons
Pibero's photons
June 18, 2008 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!!!
June 18, 2008 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
June 18, 2008 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
June 18, 2008 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)
June 18, 2008 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
June 18, 2008 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
EXTRA SLIDES
June 18, 2008 Pibero 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 5X0
• 288 SMD strips/plane/sector