1 / 48

Jet Calibration at CDF

Jet Calibration at CDF. Sandro De Cecco. Hadronic showers. EM showers. Jet Production Measurements. Unfold measurements to hadron level  Correct for efficiency, smearing. Correct theory (pQCD) for non-perturbative effects  Underlying Event, Fragmentation. Q ~  QCD. Q >>  QCD.

walkersarah
Download Presentation

Jet Calibration at CDF

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Jet Calibration at CDF Sandro De Cecco Sandro De Cecco

  2. Hadronic showers EM showers Jet Production Measurements Unfold measurements to hadron level Correct for efficiency, smearing Correct theory (pQCD) for non-perturbative effects Underlying Event, Fragmentation Q ~ QCD Q >> QCD Well defined jet algorithm required  At calorimeter, hadron and parton levels Sandro De Cecco

  3. CDF calorimeter Wall Had • Central and Wall (||<1.2): • Scintillating tile with lead (iron) as absorber material in EM (HAD) section • Coarse granularity: ~800 towers • Non-compensating • non-linear response to hadrons • Rather thin: 4 interaction lengths • Nearly no noise • Resolutions: • EM energies: /E=13.5% / √E  1.5% • HAD energies: /E=50% / √E  3% • Plug (1.2<||<3.6): • Similar technology to central • Resolution: • EM energies: /E=16 % / √E  1% • HAD energies: /E=80 % / √E  5% • Thicker: 7 interaction lengths Plug Central Sandro De Cecco

  4. below threshold (no jets) above threshold (1 jet) Cone Jet Algorithms and pQCD • Iterative cone algorithms • Staring from seeds, iteratively cluster particlesin cones of radius RCONE and look for stable cones (pT-weighted centroid) • Infrared and Collinear Safety • Fixed order pQCD contains not fully cancelled infrared divergences • Inclusive jet cross section affected at NNLO • Tevatron Run II Cone Algorithm: Midpoint • Uses midpoints between pairs of proto-jets as additional seeds  Infrared and collinear safety restored • Merging/Splitting • Emulated in NLO pQCD calculation by merging 2 partons only if they are within R’ = RCONE  RSEP of each other • Arbitrary parameter RSEP: prescription RSEP=1.3 (based on parton level approximate arguments) Sandro De Cecco

  5. jet jet jet jet jet jet jet jet kT Algorithm • Inclusive kT algorithm • Merging pairs of nearby particles in order of increasing relative pT • D parameter controls merging termination and characterizes size of resulting jets • pT classification inspired by pQCD gluon emissions • Infrared and Collinear safeto all orders in pQCD • No merging/splitting • No RSEP issue comparing to pQCD Sandro De Cecco

  6. CDF Jet calibration overview • Calibrate EM and HAD calorimeters in situ • Reconstruct jets (JetClu cone algorithm):PTraw • Correct jets in plug calorimeter w.r.t. central “relative corrections”: frel • use di-jet data (versus ) • Correct for Multiple pp Interactions : UEM • Correct measured jets back to particle level jets: tune MC simulation: fabs • Response of calorimeter to single particles • Fragmentation: Pt spectra in data • Correct for Underlying Event: UE • Correct particle jet back to parton: OC • and … Splash-out systematics Systematic error associated with each step Sandro De Cecco

  7. Two different approaches • CDF and DØ use very different approaches • Documented in • CDF Run 2: hep-ex/0510047 (accepted by NIM) • DØ Run 1: NIM A424: 352-394 (1999) • DØ Run 2: http://www-d0.fnal.gov/phys_id/jes/public/plots_v7.1/index.html • Main difference: • CDF uses test beam and single particles measured in-situ to understand absolute response of single particles • deduce jet response using simulation • Cross check with calibration processes like photon-jet data • DØ uses photon-jet data to measure absolute response • Extra correction for “showering” necessary • Other differences: • CDF corrects separately for underlying event, multiple interactions, out-of-cone energy • DØ includes all these effects into one correction factor Sandro De Cecco

  8. Overview: CDF and DØ • DØ calibrates Energy • Ecorr: calibrated jet E • Eraw: raw jet E • F: eta-dependent correction • R: absolute response • O: offset energy • includes MI, noise, UE • S: showering corrections • CDF calibrates PT • PTcorr: calibrated jet PT • PTraw: raw jet PT • F: eta-dependent correction • R: absolute response • MI: multiple interactions • Systematic error associated with each step • additional corrections to get to parton energy Sandro De Cecco

  9. In Situ Calorimeter Calibration I • Minimum Ionising Particle (MIP): • J/Ψ and W muons • peak in HAD calo: ≈2 GeV • Peak in EM calo: ≈300 MeV • Check time stability and run1 versus run2 • Applicable where muon coverage: <1.4 • E/p of electrons: • p calibrated on J/Ψ mass • Time dependence of E/p checked Sandro De Cecco

  10. In Situ Calorimeter Calibration II • Z→ ee peak: • Set absolute EM scale in central and plug • Compare data and MC: mean and resolution • Applied in Central and Plug • MinBias events: • Occupancy above some threshold: e.g. 500 MeV • Time stability • Phi dependent calibrations: resolution Sandro De Cecco

  11. Relative Corrections - dijets Sandro De Cecco

  12. Relative Corrections • Mapping out cracks and response of calorimeter • Central at ~1 by definition • D0: • Response similar in central and forward • Two rather large cracks • CDF: • Response of forward better than of central • Three smaller cracks • Difficulties: • depends on ET • Can be (most often is initially) different for data and MC Sandro De Cecco

  13. Detector to Particle Level • Do not use data since no high statistics calibration processes at high Et>100 GeV • Extracted from MC  MC needs to • Simulate accurately the response of detector to single particles (pions, protons, neutrons, etc.): CALORIMETER SIMULATION • Describe particle spectra and densities at all jet Et: FRAGMENTATION • Measure fragmentation and single particle response in data and tune MC to describe it • Use MC to determine correction function to go from observed to “true”/most likely Et: Etrue=f ( Eobs, , conesize) Sandro De Cecco

  14. Single Particle Response • Low Pt (1-10 GeV) in situ calibration: • Select “isolated” tracks and measure energy in tower behind them • Dedicated trigger • Perform average BG subtraction • Tune GFlash to describe E/p distributions at eack p (use π/p/K average mixture in MC) • High Pt (>8 GeV) uses test beam: • Could try τ-leptons • Non-linearity: response drops by 30% between 10 and 1 GeV Sandro De Cecco

  15. Fragmentation • Due to non-linearity of CDF calorimeter big difference between e.g. • 1 10 GeV pion • 10 1 GeV pions • Measure number of and Pt spectra of particles in jets at different Et values as function of track Pt: • Requires understanding track efficiency inside jets • Ideally done for each particle type (π, p, K) E.g. difference in fragmentation between Herwig and Pythia may result in different response Sandro De Cecco

  16. Absolute Correction from MC • Wanted: • Most likely true Et value for given measured Et value • BUT cannot be obtained universally for all analyses since it depends on Et spectrum: • E.g. most likely value in falling spectrum dominated by smearing from lower Et bins • Different for flat Et spectrum (e.g. top or new resonance) • CDF: • Provide standard “generic” jet corrections using flat Pt spectrum • Individual analyses determine their “specific” residual corrections themselves from their MC Sandro De Cecco

  17. Flat vs. QCD Spectra Avg Hadron Jet PT • For both spectra • There is an average PT shift of hadron jets to calorimeter jets. • With a Flat spectrum. • After accounting for the average shift there are roughly as many low PT as high PT jets “smearing” into the calorimeter PT bin. • With a QCD spectrum • After accounting for the average shift, there are significantly more low PT jets than high PT jets “smearing” into the calorimeter PT bins. • The QCD spectrum correction is therefore significantly lower. Calorimeter Jet PT Avg Hadron Jet PT Calorimeter Jet PT Sandro De Cecco

  18. Absolute Corrections • Use MC with “flat” Et distribution • Separately for each cone size: 0.4, 0.7 and 1.0 • Correction factor decreases with Et due to non-linearity of calorimeter, e.g. cone 0.4: • 50 GeV: ≈25% • 500 GeV: ≈15% • Systematic errors due to • Test beam precision • γ-Jet and Z-jet balancing agreement between data and simulation after correction  Sandro De Cecco

  19. Photon-Jet PT balance CDF pTjet/pT-1 • Agreement within 3% but differences in distributions • Data, Pythia and Herwig all a little different • These are physics effects! Sandro De Cecco

  20. Z-jet PT balance CDF pTjet/pTZ-1 • Better agreement of data and MC than in photon-jet data • In progress of understanding this better together with Herwig and Pythia authors – different L scale pTjet/pTZ-1 Sandro De Cecco

  21. Multiple pp Interactions • Extra pp interactions will increase the jet Et values of primary hard interaction • subtract off average energy in cone per interaction: • Number of interactions = Number of observed vertices • Random cone in MinBias data: Et versus Nvtx E.g. ≈0.8 GeV per vertex for cone 0.7 Sandro De Cecco

  22. Corrections from Particle Jet to Parton • Underlying event (UE) and Out-of-cone (OOC) energy • Only used if parton energy is wanted • Requires MC modeling of UE and OOC • Differences are taken as systematic uncertainty Sandro De Cecco

  23. Underlying Event • Underlying event definition: • “beam-beam remnants”: energy from interaction of spectator partons • “Initial state radiation”: energy radiated off hard process before main interaction • Not wanted when e.g. measuring the top quark mass • Can be estimated using Monte Carlo • Measurements led to tuning of MC generators: PYTHIA, Herwig+Jimmy Sandro De Cecco

  24. Measuring the Underlying Event Leading Jet Direction “Transverse” region very sensitive to the “underlying event”! • Many studies exist about underlying event: • Checkout talks by Rick Field/U. of Florida • At LHC we will need to measure it: • Expect it to be much harder than at Tevatron Sandro De Cecco

  25. Out of Cone Energy (OOC) • Out-of-Cone Energy: • Original parton energy that escapes the cone • E.g. due to gluon radiation • Jet shape in MC must describe data: • measure energy flow in annuli around jet • Differences between data and MC • Lead to rather large systematic uncertainty Herwig Pythia 0.06 0.04 Data 0.02 (OOCData-OOCMC)/pT 0.0 0.02 0.04 Herwig Pythia 0.06 40 80 120 PT (GeV) Sandro De Cecco

  26. Energy Flow Inside Jets Jet shapes governed by multi-gluon emission from primary parton • Test of parton shower models • Sensitive to underlying event structure • Sensitive to quark and gluon mixture in the final state Phys. Rev. D 71, 112002 (2005) (1-) 37 < pT < 380 GeV/c Sandro De Cecco

  27. jet jet Underlying Event & Hadronization correction Calorimeter level 1.2 Hadron level Parton level Sandro De Cecco

  28. Jet Energy Scale Uncertainties • CDF and DØ achieve similar uncertainties after following very different paths before Sandro De Cecco

  29. Other calibration samples Sandro De Cecco

  30. In-situ Measurement of JES • Additionally, use Wjj mass resonance (Mjj) to measure the jet energy scale (JES) uncertainty 2D fit of the invariant mass of the non-b-jets and the top mass: JES M(jj)- 80.4 GeV/c2 Mjj Measurement of JES scales directly with data statistics Sandro De Cecco

  31. Wjj Calibration in Top Events CDF (1 fb-1): JES = 0.99 ± 0.02 DØ (0.3 fb-1): JES = 0.99 ± 0.03 • Fit for ratio of JES in data to JES in MC • Constrain JES to 2% using 166 events At LHC will have 45,000 top events/month! Sandro De Cecco (jet)

  32. Z->bb • Zbb decay mode: • Suppresses QCD background more than signal • Difficult to trigger • CDF uses secondary vertex trigger • D0 uses semi-leptonic decays collected by muon trigger • Use this to measure difference between data and MC JES, e.g. DØ: • Data: • =81.0 +/- 2.2 • =10.7 +/- 2.1 • MC: • =83.3 • =13.0 Sandro De Cecco

  33. Using jets in analysis Sandro De Cecco

  34. W/Z bosons + Jets (HF) production • The study of W / Z / g + Jets production is very relevant: • to QCD: • High q2 ( ~Mboson) interactions, perturbative theory • Less leading diagrams ( wrt pure Jets production) • Test Monte Carlo generators (ALPGEN…) • Probe for protons PDF (in particular W/Z + H.F. ) • for many high pT analysis where W/Z+HF is a main background: • Top quark cross section and mass • Single Top cross section • Search for low mass Higgs boson • several SUSY searches Sandro De Cecco

  35. W+jet(s) Production • Background top, Higgs, SUSY… • Stringent test of pQCD predictions • Test Ground for ME+PS techniques • (Special matching  MLM, CKKW to avoid • double counting on ME+PS interface) ..+ PS Affected by cutoff and soft radiation Sandro De Cecco

  36. Soft radiation in Z+jet(s) Z jet Implementation of proper modeling of UE still needed in W/Z+Jet(s) Monte Carlo….very important LHC will use "extra jets" veto in Higgs analyses to reduce QCD bckg. Sandro De Cecco

  37. Phys. Rev. Lett. 95, 022003 (2005) Diphoton Production relevant for future H-> gg searches at the LHC (K-factors applied to LO pQCD MCs ) LO (Pythia) off g q g q g g q g q g g NLO (DIPHOX) g g Sandro De Cecco

  38. Electrons and Jets Hadronic Calorimeter Energy • Jets can look like electrons, e.g.: • photon conversions from 0’s: ~13% of photons convert (in CDF) • early showering charged pions • And there are lots of jets!!! Electromagnetic Calorimeter Energy Sandro De Cecco

  39. Isolation is very powerful for isolated leptons E.g. from W’s, Z’s Rejects background from leptons inside jets due to: b-decays photon conversions pions/kaons that punch through or decay in flight pions that shower only in EM calorimeter This is a physics cut! Efficiency depends on physics process The more jet activity the less efficient Depends on luminosity Extra interactions due to pileup Isolation cut: Draw cone of size 0.4 around object Sum up PT of objects inside cone Use calorimeter or tracks Typical cuts: <10% x ET <2-4 GeV The Isolation Cut  candidates Non-isolated isolated Sandro De Cecco

  40. Jets faking Electrons • Jets can pass electron ID cuts, • Mostly due to • early showering charged pions • Conversions:0ee+X • Semileptonic b-decays • Difficult to model in MC • Hard fragmentation • Detailed simulation of calorimeter and tracking volume • Measured in inclusive jet data at various ET thresholds • Prompt electron content negligible: • Njet~10 billion at 50 GeV! • Fake rate per jet: • Loose cuts: 5/10000 • Tight cuts: 1/10000 • Typical uncertainties 50% Jets faking “loose” electrons Fake Rate (%) Sandro De Cecco

  41. Feynman Talk at Coral Gables(December 1976) 1st transparency Last transparency “Feynman-Field Jet Model” Sandro De Cecco

  42. Backup Sandro De Cecco

  43. Underlying Event • Everything but the hard scattering process • Initial state soft radiations • Beam-beam remnants • Multiple Parton Interactions (MPI) • Studied in the transverse region • Leading jet sample • Back-to-back sample Sandro De Cecco

  44. New Underlying Event Studies MB ET(jet#2)/ET(jet#1) > 0.8 Back-to-Back Df12 > 150o Suppresses contribution from additional hard radiation Pythia Tune A describes the data Herwig underestimates UE activity Extended to 250 GeV jets Sandro De Cecco

  45. New Underlying Event Studies • MAX (MIN) for the largest (smallest) charged particle density in transverse region • MAX-MIN sensitive to remaining hard contribution • MIN specially sensitive to the remnant-remnant contribution Sandro De Cecco

  46. Studies on Jet Fragmentation • Jet shape dictated by multi-gluon emission form primary parton • Test of parton shower models and their implementations • Sensitive to quark/gluon final state mixture and run of strong coupling • Sensitive to underlying event structure in the final state Sandro De Cecco

  47. Jet shapes PRD. 71, 112002 (2005) • PYTHIA Tune A describes the data • (enhanced ISR + MPI tuning) • PYTHIA default too narrow • MPI are important at low Pt • HERWIG too narrow at low Pt We know how to model the UE at 2 TeV for QCD jet processes Sandro De Cecco

  48. PRL 94, 221801 (2005)  Studies on Δfbetween jets LO in Df NLO in Df LO limited at hard (Mercedes Star) and soft limits for third emission NLO closer to data…however soft gluon contributions are needed PYTHIA (enhanced ISR) & Herwig provide best description across the different regions… Sandro De Cecco

More Related