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Jet Energy and Resolution at the CDF-Run II experiment

Jet Energy and Resolution at the CDF-Run II experiment. Monica D’Onofrio ATLAS-IFAE meeting, 9/15/2006. Outline. Introduction CDF experiment and calorimeter Jet Energy Scale correction: method Calorimeter response h -dependent corrections Multiple interactions

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Jet Energy and Resolution at the CDF-Run II experiment

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  1. Jet Energy and Resolution at the CDF-Run II experiment Monica D’Onofrio ATLAS-IFAE meeting, 9/15/2006

  2. Outline • Introduction • CDF experiment and calorimeter • Jet Energy Scale correction: method • Calorimeter response • h-dependent corrections • Multiple interactions • Underlying event and Out-of-cone Energy • Cross check of jet energy scale (photon/Z+jet data) • Other calibration signals Monica D' Onofrio, ATLAS-IFAE meeting

  3. Motivation (1) • Knowledge of Jet Energy Scale (JES) is fundamental for hadron colliders • All physics processes involve jets that span a wide ET range [0,√s/2] • Important for SM measurements … Inclusive jet cross section Jet Energy Scale uncertainties are dominant for high PT jets Monica D' Onofrio, ATLAS-IFAE meeting

  4. Missing ET Multiple jets Missing ET Motivation (2) • … also most of Non-Standard Model signatures (i.e. squark-gluino production) involve jets and Missing Transverse Energy (MET)  MET must be corrected for jet energy measurements. • Procedure to determine JES and uncertainties is not settled yet in ATLAS and CMS  Experience from CDF as example Correction ~ 12% at low MET (from Xavier’s) Monica D' Onofrio, ATLAS-IFAE meeting

  5. Tevatron and CDF@RunII Highest-energy accelerator currently operational CDF Peak luminosity  above 2.0 *1032 cm-2 s-1 Integrated luminosity/week  about 25 pb-1 CDF: ~1.5 fb-1on tape • Silicon microvertex tracker •  Excellent tracking efficiency • Solenoid • High rate trigger/DAQ • L2 trigger on displaced vertices • Calorimeters and muons Monica D' Onofrio, ATLAS-IFAE meeting

  6. CDF Calorimeter • Central and Wall ( |h|<1.2 ): • Granularity: Df × Dh= 15° × 0.1 (~ 800 towers) • Non compensating •  non-linear response to hadrons • Rather thin: 4 interaction lenghts • Small amount of noise • Resolutions: • EM energies (g,e): s/ET = 13.5%/√ET+1.5% • HAD energies(p±): s/ET = 50%/√ET+3% • Plug (1.2<|h|<3.6): • Similar technology to the central • Resolutions: • - EM energies (g,e): s/E = 16%/√E+1% • - HAD energies (p±): s/E = 80%/√E+5% • Thicker than central: 7 interaction lenghts Monica D' Onofrio, ATLAS-IFAE meeting

  7. Calorimeter calibration: EM energy • Check calorimeter response: • Use test beam (from 1980s!) and single particles measured in-situ to understand absolute response • Check time dependence • For EM energy response use: • MIP peak when possible • (at about 300 MeV) • Ze+e- mass peak stability • Set absolute EM scale • in central and plug Monica D' Onofrio, ATLAS-IFAE meeting

  8. Calorimeter calibration: Hadronic Energy • For hadron energy response use • Minimum Ionizing Particles (MIP): • J/ and W muons • Peak HAD calorimeter: ~ 2 GeV Also Minimum bias events: - E.g. N towers (ET>500 MeV) Syst. Uncertainty related to Calorimeter Calibration ~ 0.5% Monica D' Onofrio, ATLAS-IFAE meeting

  9. Jet reconstruction • A jet is a composite object: • complex underlying physics • depends on jet definitions • Use different kind of Jet algorithms: • - Cone algorithms (JETCLU and MIDPOINT) • - KT algorithm • Corrections on Jet Energy Scale (JES) for • different effects: • Instrumental effects: • - response to hadrons • - poorly instrumented regions • - Multiple p-pbar interactions • Physics effects: - Underlying event - Hadronization Time Monica D' Onofrio, ATLAS-IFAE meeting

  10. CDF Jet Energy Scale Method Different correction factors: • (frel)Relative Corrections  Make response uniform in h : all corrections are then referred to the central region • (MPI)Multiple Particle Interactions  Energy from different ppbar interaction • (fabs)Absolute Corrections  Calorimeter non-linear and non-compensating PT jetparticle(R) = [ PT jetraw(R)  frel (R) – MPI(R)]  fabs(R) • Additional corrections to get to parton energy: • (UE)Underlying Event • Energy associated with spectator partons in a hard collision • Hadron-to-Parton correction(historically defined as Out-Of-Cone) PT parton(R) = PT jetparticle(R) - UE(R) + OOC Systematic uncertainties are associated with each step Monica D' Onofrio, ATLAS-IFAE meeting

  11. Calorimeter simulation • Use MC simulation to determine Jet Corrections • MC needs to • Simulate accurately detector response to single particle (E/p). CDF uses: • GEANT to track generated particles through the detector • Gflash for fast EM and HAD shower simulation, using parametrizations of longitudinal and lateral shower profiles • Describe jet fragmentation: MC tuned on data • Tuning based on in-situ CDF data(dedicated triggers) • E/P response as a function of particle momentum p. • Lateral profile shower Monica D' Onofrio, ATLAS-IFAE meeting

  12. Single particle response Test beam In situ: Select ‘isolated’ tracks Measure energy in tower behind them Dedicated trigger Bgk subtraction Tune simulation to describe E/p distribution at each p Single particle response simulation Monica D' Onofrio, ATLAS-IFAE meeting

  13. Single particle response simulation • Jet composition: • ~ 70 % charged particles • - 10% protons • - 90% pions • 30 % neutral pions (gg) • - EM response hadrons • Remaining difference data/simulation  taken as syst. uncertainty Monica D' Onofrio, ATLAS-IFAE meeting

  14. Uncertainties on calorimeter simulation Improvement possible with higher statistical samples Total uncertainties: Sensitive to 0.9x0.9 = 81% inner part of the tower.  For tower boundaries: additional 10% uncertainty Monica D' Onofrio, ATLAS-IFAE meeting

  15. Lateral profile • Measure E/p signal in 5 towers adjacent in h • signal defined as 1×3 strip in φ • Plot E/p vs. relative eta for 5 towers • In Gflash, use formula for lateral profile • EM and HAD calorimeter probe different parts of the hadronic shower excluding 90° crack E/p vs ηrel (Central) Monica D' Onofrio, ATLAS-IFAE meeting

  16. Fragmentation • MC simulation needs to reproduce well data: • Due to non-linearity of the calorimeter, • non trivial correlation N particles and PT track spectra: • - one 10 GeV pion: ~ 8 GeV • - ten 1 GeV pions: ~ GeV • Very important a good understand of track efficiency • Measurement of jet shape is fundamental Integrated jet shape Data/MC different = Systematic uncertainty ~ 1% Monica D' Onofrio, ATLAS-IFAE meeting

  17. CDF Jet Energy Scale Method Detector effects correction: (frel)Relative Corrections Make response uniform in h (MPI)Multiple Particle Interactions Energy from different ppbar interaction (fabs)Absolute Corrections Calorimeter non-linear and non-compensating PT jetparticle(R) = [ PT jetraw(R)  frel (R) – MPI(R)]  fabs(R)

  18. Relative Corrections • Jet Corrections are relative to the central calorimeter: • Central (0.2<|h|<0.6 jets) ~1 by definition (reference) • Difference Data/MC mainly in the forward region  Depends on ET jets considered cracks Monica D' Onofrio, ATLAS-IFAE meeting

  19. Multiple Interactions • Overlapping interactions can overlap the jet • Number of extra interactions depends on luminosity • LHC • Low lumi (L = 1× 1033 cm-2 s-1): <N>=2.3 • High lumi (L = 1× 1034 cm-2 s-1): <N>=23 • Tevatron • L = 2× 1032 cm-2 s-1: <N>=6  Offset depending on number of interactions Monica D' Onofrio, ATLAS-IFAE meeting

  20. Multiple Interaction corrections • Linear correlation between number of interactions and number of vertices • Define random cones in the central region (0.2<|h|<0.6) and determine average • transverse energy associated to a cone • Cone-based method: should improve to make it more general (KT?) For cone R = 0.7, <ET> = 1.06 GeV Monica D' Onofrio, ATLAS-IFAE meeting

  21. Jet corrections to particle level (absolute) • Monte Carlo simulation used to compare measured (calorimeter) jets and particle (hadron) jets. • Depends on MC simulation and how well data are reproduced, and on fragmentation • Main uncertainties due to calorimeter simulation Monica D' Onofrio, ATLAS-IFAE meeting

  22. Absolute Correction • Almost independent on jet cone size. • Depends on transverse momentum: calorimeter response is ~ 70% for 25 GeV/c jets, ~ 90% for 400 GeV/c jets. Absolute correction factor Monica D' Onofrio, ATLAS-IFAE meeting

  23. Cross-check using prompt photons • Photons are well measured in EM calorimeter • Complications: • number of events at high ET very low • From D0 measurement, 40 evt. with L=1 fb-1 and ETg > 300 GeV • Background due to p0 • Purity 30-80 % for [20-100] GeV photon transverse energy range • In CDF: use photon+jets (but also Z+jets) for cross check and to evaluate OOC corrections and JES systematic uncertainty due to Data/MC differences. Monica D' Onofrio, ATLAS-IFAE meeting

  24. Data • Pythia • Herwig g (Z) + jet pT balance • ET leading jet > 25 GeV • ET (second jet) < 3 GeV • Df (Jet-g) > 3 Sensitive to radiation effects when allow second jet: Herwig farther away from jet cone pT balance: Agreement Data/MC within 3% Monica D' Onofrio, ATLAS-IFAE meeting

  25. Z-jet pT balance • These events allow us to reach lower PT than photon+jet and also cross check photon+jets results. • Selection • two e(m) with ET>18 GeV (pT>20 GeV) • 76 < M ee(mm) < 106 GeV • ET leading jet > 25 GeV • ET (second jet) < 3 GeV • Df (Jet-Z) > 3 Similar Herwig behaviour for Z+jet w.r.t. g+jet but less visible Monica D' Onofrio, ATLAS-IFAE meeting

  26. Model-dependent corrections • Underlying event (UE) and Hadron-to-Parton (Out-of-cone, OOC) energy corrections used only if need parton energy • Modeling are required, difference MCs as systematic uncertainties. • Method might be different depending on analysis (top mass reconstruction, Higgs boson searches) • Parton transverse momentum: PT parton(R) = PT jetparticle(R) - UE(R) + OOC Monica D' Onofrio, ATLAS-IFAE meeting

  27. Underlying event • Particle jet could have contributions note related to hard interaction: • Beam-beam renmants • Initial state radiation • MC tuned on Data (as Pythia Tune A) • Use di-jet events Will be much harder at the Tevatron!!! Monica D' Onofrio, ATLAS-IFAE meeting

  28. Out-of-Cone Correction • OOC energy: energy escaping the cone radius • Gluon radiation (FSR) • Obtained from Pythia di-jet samples: • Ratio PTparton / PT jet particle • Similar performance Pythia and Herwig • Systematic uncertainties from photon+jet events: • Assume PTg = PT jet corr. • Difference Data/MC of energy inside annuli around jet axis taken as systematic uncertainty Monica D' Onofrio, ATLAS-IFAE meeting

  29. JES Systematic uncertainties Total systematic uncertainties for JES  between 2 and 3% as a function of corrected transverse jet momentum High Pt: Dominated by calorimeter simulation uncertainties Low Pt: Dominated by MC/data uncertainties Monica D' Onofrio, ATLAS-IFAE meeting

  30. Possible improvements • Absolute energy scale: • Better simulation translate in lower systematic uncertainties: • Old simulation (response as function of track momentum): • [0-12] GeV  2.5%, [12-20] GeV 3%, [20,+]  4% • New simulation (under study): • 2% expected in the whole p range This would reduce absolute JES uncertainty from 1.8-2.5% to1.4% • Specific b-jet correction • Using Zbbbar or photon+b • Jet resolution for higgs analysis • H1 algorithm: use tracking information for energy determination of charged hadrons Monica D' Onofrio, ATLAS-IFAE meeting

  31. Extraction of signal • b-jet energy scale, tools to extract DiJet mass resonances (Hbb) • Good signal to study/improve/confirm jet resolution • Trigger on two displaced tracks+ two 10 GeV jets , L = 333 pb-1, 21.5 M events • DisplacedVertex tag, SecondryVertex Mass to select b-jets, kinematic cuts to improve S/B • Fit signal and background (direct QCD production) templates, for varying JES DiJet Invariant mass GeV Further Studies: Extract b-specific correction Evaluate systematic biases. Improve Zbb modeling Stat error is ~2%. Selected Events 85730 Zbb 3394±515 (4%) Monica D' Onofrio, ATLAS-IFAE meeting

  32. Jet Resolution (H1 Algorithm) • Apply relative corrections to make response flat in η. • Use tracks (0.5<Pt<15 GeV, Pt ordered), extrapolate to face of calorimeter • Select towers within Δη=0.1 and Δφ=0.2. (Central towers are 0.1x0.26.) Take the nearest tower one if none within these limits. • Order selected towers in distance from the track. • Remove towers such that corresponding removed energy is always less or equal to the energy of the track. Energy already removed by a previous track is not considered by subsequent tracks. • Jet is sum of all quality-selected tracks and remaining towers in the jet. • Scale the final jet energy • There is improvement (10-15%) but need much more work for optimization. Monica D' Onofrio, ATLAS-IFAE meeting

  33. Calibration Peaks from W’s or Z’s Very difficult to see inclusive decays of W and Z in jets Best possibilities: - W from top decays - Z in bb decay mode Monica D' Onofrio, ATLAS-IFAE meeting

  34. Summary and Conclusions • CDF tunes simulation and derives corrections from MC  different procedures can be chosen • Many ‘calibration’ signals: • MIP peak, Zee and Minimum Bias for calorimeter • Di-jet balance for relative response in cracks and plug • Isolated tracks for calorimeter response • Photon-jet balance for cross-check and systematic uncertainties • 3% systematic uncertainty achieved • Can do better: we are improving calorimeter simulation and trying to better understand difference Data/MC  expect to lower JES uncertainty to 2% Monica D' Onofrio, ATLAS-IFAE meeting

  35. Back-up

  36. Jet Algorithms Monica D' Onofrio, ATLAS-IFAE meeting

  37. Clusters using different Jet algorithms Monica D' Onofrio, ATLAS-IFAE meeting

  38. Lateral profile Monica D' Onofrio, ATLAS-IFAE meeting

  39. Lateral profiles scan Monica D' Onofrio, ATLAS-IFAE meeting

  40. Calorimeter simulation • Use MinBias or isolated track trigger • Select good tracks within central 81% of tower. • No extra track within 7x7 towers, no ShowerMax cluster. • Measure E/p in data • Tune Gflash parameters • Difference in data and simulation is taken as uncertainty. E(HAD)/p E(EM)/p E(Total)/p After BG subtraction More statistics! Monica D' Onofrio, ATLAS-IFAE meeting

  41. Photon+jet balancing Herwig Pythia Data -0.371 -0.317 -0.360 Δφ> 3, No 2nd jet cut • PT balance between photon and jet is about 3% different among data and MC. Δφ>3 , second Jet Pt<3 GeV Herwig Pythia Data -0.328 -0.296 -0.306 Monica D' Onofrio, ATLAS-IFAE meeting

  42. Photon/Z – jet balance Monica D' Onofrio, ATLAS-IFAE meeting

  43. Calorimeter simulation improvements • Tower-phi boundaries improved with new Simulation from 10% uncertainty to less than 5% • Old simulation • New Simulation  Monica D' Onofrio, ATLAS-IFAE meeting

  44. JES on top mass with in-situ Wjj Monica D' Onofrio, ATLAS-IFAE meeting

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