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Electron/Photon group overview

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

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Electron/Photon group overview

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  1. Electron/Photon group overview US CMS Meeting, Princeton April 30, 2004 Rick Wilkinson, Caltech

  2. USCMS in e/gamma • UCSD: Higgs gg • Jim Branson, Satyaki Bhattacharya, James Letts, Kyle Armour • Caltech: Higgs gg, h gg 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

  3. Higgs gg (Caltech) • Traditional, cuts-based counting experiment • Background simulation uses generator-level preselection • Look for g, p0, e, h, h’, r, w • Saves factor of ~3000 in CPU for QCD background • Resulting luminosity required 5s discovery: • Inclusive Higgs production (pp  H gg) 39.2 fb-1 • Also look for Vector Boson Fusion • pp  qqH  qqgg • Has two forward jets with |h| ~ 3 • Surprisingly good discovery reach, 41 fb-1

  4. background signal log(s/b) Higgs gg (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! • 5s discovery only needs: • 2 fb-1 for jet-jet bg • 2 fb-1 for g-jet bg • 0.5 fb-1 for gg bg • Need to combine somehow • Too good?

  5. H gg 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

  6. H gg Kinematics Neural Net • Neural Net Inputs are: • Jet-jet and g-jet • Calo isolation, track isolation, ET1/(ET1+ET2), ET2, |h1-h2| • Irreducible background • ET1, ET2, ESC1, ESC2, |h1-h2| • S/B obtained from the black fitted curves γ-jet cat1 (cleanest) background signal

  7. background signal log(s/b) H gg Mass 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

  8. Calibration • Baseline: Track momenta from electrons from W decay • Problem: Can we avoid strict cuts on brem? • May take months • Other, faster techniques • hgg(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 f-symmetry + Z  ee • See next slide

  9. Calibration • Start by looking at f-symmetry, comparing summed energy in crystals around a ring in h (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) SETvs f: All rings combined

  10. 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

  11. e/gamma code: Physics Objects Persistent 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

  12. 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) Why this difference in the energy distribution ? OSCAR CMSIM Emeas/Etrue

  13. <2nd sub-module> <16th sub-module> back leakage ~26 radn. lengths <<26 radn. lengths front leakage Electrons: Resolution vs. h • ECAL Barrel resolution gets worse with h. (Takahashi, ICL) Doesn’t seem to be because of lateral shower spread. • Maybe back or front leakage?

  14. 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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