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Electron/Photon group overview. US CMS Meeting, Princeton April 30, 2004. Rick Wilkinson, Caltech. USCMS in e/gamma. UCSD: Higgs gg Jim Branson, Satyaki Bhattacharya, James Letts, Kyle Armour Caltech : Higgs gg, h gg calibration - PowerPoint PPT Presentation
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Electron/Photon group overview
US CMS Meeting, Princeton
April 30, 2004
Rick Wilkinson, Caltech
USCMS in e/gamma
• UCSD: Higgs – Jim Branson, Satyaki Bhattacharya, James Letts, Kyle Armour
• Caltech: Higgs calibration– Harvey Newman, Sergey Shevchenko, Vladimir Litvin, Tony Lee
• Caltech: Calorimetry core software– Vladimir Litvin, Rick Wilkinson
• Yale: Calorimetry core software– Homer Neal
• Minnesota: Simulation– Maria Obertino
• UC Riverside: e/gamma software, calibration– David Futyan
Higgs (Caltech) • Traditional, cuts-based counting experiment• Background simulation uses generator-level preselection
– Look for e’– Saves factor of ~3000 in CPU for QCD background
• Resulting luminosity required 5discovery:– Inclusive Higgs production (pp H ) 39.2 fb-1
• Also look for Vector Boson Fusion– pp qqH qq– Has two forward jets with || ~ 3
– Surprisingly good discovery reach, 41 fb-1
Higgs (UCSD) • More aggressive; avoid cuts. Keep all the information you can.• Sort events by their cleanliness, using
– Photon quality (narrowness of the worst one)
– Kinematics, using a neural net
– Even use the lineshape of the Higgs mass hypothesis!
• Combine all these factors into a S/B estimate for the event• Plot the event by its S/B
• Results are amazingly good!– 5 discovery only needs:
• 2 fb-1 for jet-jet bg
• 2 fb-1 for -jet bg
• 0.5 fb-1 for bg
– Need to combine somehow– Too good? log(s/b)
background
signal
H photon quality• Categorize events by the quality of
their worst photon.
• r9 = (Sum of 9)/ESC (uncorrected)
– 4 bins in narrowness r9
x2 bins (barrel, endcap)
makes 8 categories of events
• Better photons have– better mass resolution
– Less QCD background
• Analyze event categories separately– Only combine in final plot
signal
unconverted
background
H Kinematics Neural Net
• Neural Net Inputs are:– Jet-jet and -jet
• Calo isolation, track isolation, ET1/(ET1+ET2), ET2, |1-2|
– Irreducible background
• ET1, ET2, ESC1, ESC2, |1-2|
• S/B obtained from the black fitted curves
γγ-jet cat1 -jet cat1 (cleanest)(cleanest)γγ-jet cat1 -jet cat1 (cleanest)(cleanest)
background
signal
H ass shape & discovery reach
• Include mass information in s/b• Fit resulting plot for signal,
background
• Do many trials:– background-only experiments
– signal+background.
• Some overlap– Luck will play a role in how
fast we find the Higgs
log(s/b)
backgroundsignal
Calibration• Baseline: Track momenta from electrons from W decay
– Problem: Can we avoid strict cuts on brem?– May take months
• Other, faster techniques– (V. Litvin & S. Shevchenko, Caltech)
• Photons usually separated by 3-10 crystals
• Needs a day or two of dedicated running with the full DAQ bandwidth!
– Combine -symmetry + Z ee• See next slide
Calibration• Start by looking at -symmetry, comparing summed
energy in crystals around a ring in D. Futyan, UCR)– In min-bias events
• Too low energy?• Sensitive to tracker material
– In jet triggers• Trigger biases
– trigger region boundaries!
• Then, calibrate between the rings with Z ee (Rome)– 170 parameters in barrel, 80 in endcap– Math. Lots of math. (Iterative algorithm now, others possible)
All rings combined
ETvs
Calorimetry Software
• Skeleton transplant in progress!– Switching to common framework with Tracker, Muon– Allows us gain functionality they already have:
• Track propagation• DAQ readout grouping• Misalignment
• To-do list:– Calibration constants– Analyze HCAL testbeam data with ORCA
e/gamma code: Physics ObjectsPersistent Physics Objects in DST data for Data Challenge ’04 datasets:(D. Futyan, E. Meschi)• EGBCluster (basic cluster)• ET threshold gives EGCluster• Brem recovery gives EGSCluster (supercluster)• Endcap preshower gives EGECluster• Fiducial cuts give EgammaCandidates
– Offline • EgCandFromEGSCluster• EgCandFromEGECluster
– Level 2 trigger• EgCandL2FromEGSCluster• EgCandL2FromEGECluster
• If there’s an associated pixel track: EGElectron• If no associated pixel track, EGPhoton• Also EPTrack, EgammaMC
GEANT4/OSCAR Validation
• Long-running mystery about the electron energy resolution.
• There was a bug in the simulation thresholds used in the material description. Tracker cooling ledges were opaque to their own brem. (M. Obertino)
CMSIMOSCAR
Emeas/Etrue
Why this differencein the energy
distribution ?
Electrons: Resolution vs.
• ECAL Barrel resolution gets worse with TakahashiICL) Doesn’t seem to be because of lateral shower spread.
• Maybe back or front leakage?
<2nd sub-module> <16th sub-module>
back leakage
front leakage
~26 radn.lengths
<<26 radn.lengths
High Energy Electrons• For Randall-Sundrum graviton studies (Collard, Lemaire)• Need to re-optimize clustering algorithms & corrections• Synchrotron radiation not a problem• ADC saturation is a problem, but can be corrected
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