1 / 56

Jet Energy and calibration with data at the CDF experiment

This presentation at the Top Workshop discusses the importance of jet energy scale calibration at CDF experiment. Topics covered include jet energy scale correction methods, calorimeter response, h-dependent corrections, absolute corrections, multiple interactions, and calibration signals for top and B-specific jet corrections. The impact of jet energy scale on measurements, especially the top mass, is addressed. Details on the CDF experiment, jet reconstruction, calorimeter calibration for EM and hadronic energies, simulation techniques using MC data, and uncertainties on calorimeter simulation are also presented.

kcrowley
Download Presentation

Jet Energy and calibration with data at the CDF experiment

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 Energy and calibration with data at the CDF experiment Monica D’Onofrio 3rd Top Workshop, from the Tevatron to ATLAS Grenoble, 10/23/2008

  2. Outline • Introduction • CDF experiment and calorimeter • Jet Energy Scale correction: method • Calorimeter response • h-dependent corrections • Absolute correction • Multiple interactions • Underlying event and Out-of-cone Energy • Cross check of jet energy scale (photon/Z+jet data) • Other calibration signals: In-situ calibration for top • B specific jet corrections with Zbbbar Note: MC samples used in JES  PYTHIA 6.216 Tune A and HERWIG 6.5 Top Workshop@Grenoble, 10/23/2008

  3. Motivation • 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 … Phys Rev D75, 092006 (2007) Inclusive jet cross section Jet Energy Scale uncertainties are dominant for high PT jets A 1% uncertainty on the jet scale translates to ~10% uncertainty for jet C.S. ~500 GeV PT and O(1%) on the top mass Top Workshop@Grenoble, 10/23/2008

  4. Impact on top mass Mainly dominated by Underlying Event and Parton Shower uncertainties • Dilepton Top Mass channel:  JES dominant systematic • In lepton+jets and all hadronic channels, in-situ calibration is possible using the well-measured W boson mass: • JES systematics not dominant • still an important factor • Lepton+jets (DLM) Top Workshop@Grenoble, 10/23/2008

  5. Tevatron and CDF@RunII Highest-energy accelerator currently operational CDF Peak luminosity  above 3.1 *1032 cm-2 s-1 Integrated luminosity/week  about 40-60 pb-1 CDF: ~4.2 fb-1on tape • Silicon micro-vertex tracker •  Excellent tracking efficiency • Solenoid • High rate trigger/DAQ • L2 trigger on displaced vertices • Calorimeters and muons Top Workshop@Grenoble, 10/23/2008

  6. 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 Top Workshop@Grenoble, 10/23/2008

  7. 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 Top Workshop@Grenoble, 10/23/2008

  8. 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 Top Workshop@Grenoble, 10/23/2008

  9. 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% Top Workshop@Grenoble, 10/23/2008

  10. 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 Top Workshop@Grenoble, 10/23/2008

  11. 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 Top Workshop@Grenoble, 10/23/2008

  12. 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 Top Workshop@Grenoble, 10/23/2008

  13. Uncertainties on calorimeter simulation Improved now with higher statistical samples up to p~40 GeV/c Sensitive to 0.9x0.9 = 81% inner part of the tower.  For tower boundaries: additional 10% uncertainty Total uncertainties: Top Workshop@Grenoble, 10/23/2008

  14. 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 Top Workshop@Grenoble, 10/23/2008

  15. 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 Top Workshop@Grenoble, 10/23/2008

  16. h-dependent corrections • PT-balancing also used to implicitly correct for transverse spreading of calorimeter showers outside jet cone + any h dependence of gluon radiation and multiple parton interactions Systematic Uncertainties After corrections Data Pythia Top Workshop@Grenoble, 10/23/2008

  17. 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 Top Workshop@Grenoble, 10/23/2008

  18. 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 For cone R = 0.7, <ET> = 1.06 GeV Top Workshop@Grenoble, 10/23/2008

  19. CDF Absolute Corrections • Use MC simulation  MC is adjusted by comparison with data to: • simulate accurately detector response to single particle (E/p). • Due to non-linearity of the calorimeter, non trivial correlation between N particles and PT track spectra • Very important a good understand of track efficiency • describe jet fragmentation • Measurement of jet shape is fundamental Integrated jet shape Data/MC difference  Systematic uncertainty ~ 1% Top Workshop@Grenoble, 10/23/2008

  20. Mapping the absolute scale • Map the calorimeter jet PT to the particle jet PT • Use di-jet events generated in PYTHIA (0-800 GeV). • Based on the most probable observed jet transverse momentum PTjet, given a particle jet with fixed value PTparticle • Probability density function dP(PTparticle,PTjet): where DPT = Ptparticle – Ptjet and remaining parameters are used to model a double Gaussian function representing a core response and tails Top Workshop@Grenoble, 10/23/2008

  21. Jet corrections to particle level (absolute) Unbinned likelihood fit used to extract the response parameters • 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 After this correction, jets are independent of the detector. Top Workshop@Grenoble, 10/23/2008

  22. Jet Fragmentation Fraction of tracks VS P • If E/p was flat, uncertainty in Pt spectrum of particles in the jet will not lead to any uncertainty in energy scale. • Momentum distribution of charged tracks distribution in data and Pythia MC agree except at low momenta. • However, for same measured jet , total energy carried by charged tracks is different in data and Pythia (~a few %). • Pythia/Data scale differ by <1% for 20-220 GeV jets. Take as systematic uncertainty. • HERWIG and PYTHIA agree to better than 1%. Top Workshop@Grenoble, 10/23/2008

  23. Absolute systematic uncertainties FRAGMENTATION(1%)takes into account how well MC describe the particle spectra and densities at all Jet ET CALORIMETER SIMULATION (1.3  2.5%) uncertainty on response of the calorimeter to single particles (p, p, n, etc) STABILITY (0.5%) Calorimeter scale variation with time Top Workshop@Grenoble, 10/23/2008

  24. 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 Top Workshop@Grenoble, 10/23/2008

  25. Underlying Event • Particle jet could have contributions 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 LHC!!! Top Workshop@Grenoble, 10/23/2008

  26. 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 Take the largest difference between Data/PYTHIA and Data/HERWIG  Up to 6% for low PT jets Top Workshop@Grenoble, 10/23/2008

  27. 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 Top Workshop@Grenoble, 10/23/2008

  28. Cross-checks using prompt photons • Photons are well measured in EM calorimeter • Complications: • number of events at high ET very low • Background due to p0 • Purity 30-80 % for [20-100] GeV photon transverse energy range • Use photon+jets (but also Z+jets) for cross check and to evaluate OOC corrections and JES systematic uncertainty due to Data/MC differences. Top Workshop@Grenoble, 10/23/2008

  29. Data • Pythia • Herwig g (Z) + jet pT balance • pT photon > 27 GeV (trigger) • 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% Top Workshop@Grenoble, 10/23/2008

  30. 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 (second jet) < 3 GeV • Df (Jet-Z) > 3 Similar Herwig behaviour for Z+jet w.r.t. g+jet but less visible pT (Z) Top Workshop@Grenoble, 10/23/2008

  31. Comments on CDF procedure • CDF procedure demands a very accurate simulation of calorimeter showers and good understanding of underlying physics. It requires a detailed understanding of material in tracking volume, calorimeter response to single particles as well as particle Pt spectrum in jets (good knowledge of track reconstruction efficiency in high multiplicity environment). A lot of work has been done! • Ensuring good simulation implies that simulated data can be directly compared with real data in the variable of one’s choice (e.g. size of τ Jet). • Easy to build upon to improve jet resolution. • Same procedure valid for all Pt Jets, even at 500 GeV jets. • The procedure depends only on jet/calorimeter simulation and is independent of extra radiation in the event. • Various stages of corrections allow users to do analysis at calorimeter-level, particle-level, particle-level after UE subtraction, parton-level, depending on physics. Top Workshop@Grenoble, 10/23/2008

  32. Contraining JES with Dijet mass resonances & b-jet specific corrections - W from top decays - Z in bb decay mode

  33. Calibration Peaks from W’s In situ calibrations in ttbar samples:  take the single dijet mass closest to the well known W mass as the single value of mjj per event Top Workshop@Grenoble, 10/23/2008

  34. JES on top mass with in-situ Wjj One example (lepton+jets Multivariate method) mt = 172.2 ± 1.0 (stat.) ± 0.9 (JES) GeV/c2 Measured value for ΔJES, which is: ΔJES = 0.09 ± 0.29 σ • BUT: “One point” calibration  cannot constrain JES over wide PT range. • (residual dependence on jet PT and h) • Not specific for b-jet • (b-specific uncertainty) Top Workshop@Grenoble, 10/23/2008

  35. B-jet energy scale • B-jet response is expected to be different from light quarks or gluon jets responses: • Harder fragmentation • B-hadron decays (semileptonic fraction) • Dependence on tagging procedure used to identify b-jets • Use MC to model b-jet response • Apply generic jet energy corrections • Additional corrections (from MC) to correct b-jets back to the parent b-quark • Imply additional uncertainties (~0.4-0.6%) • based on constraints from other experiments • Test in data using g+bjet or Zbbbar Top Workshop@Grenoble, 10/23/2008

  36. Z  bbbar signal Published on NIM doi:10.1016/j.nima.200.08.133 • b-jet energy scale from Z signal • tools to extract DiJet mass resonances (Hbb) • Trigger on two displaced tracks+ two 10 GeV jets • DisplacedVertex tag (2-tags required) , SecondaryVertex Mass to select b-jets • kinematic cuts to improve S/B • Fit signal and background (direct QCD production) templates, for varying JES 1 tag extract a signal of N = 5674 ± 448(stat.) Z → bbbar decays in low-radiation central dijet events 2 tag Top Workshop@Grenoble, 10/23/2008

  37. b-jet energy scale from Z  bb Constrained unbinned likelihood fit performed to double tagged dijets data. Signal constrained to Nexp. events (gaussian constraint: 4630 ± 727 Gaussian). DiJet Invariant mass σZ x BR(Z->bb) = 1578 ± 123(stat)+624-391(syst) pb = 1578 +636-410(stat+syst) pb. Theory (NLO): σZxBR(Z->bb) = 1129 ± 22 pb B-Jet energy scale: 0.974 ± 0.011 ( stat.) ± 0.017 (sys.) (agreement with 1 sigma of nominal scale factor) Top Workshop@Grenoble, 10/23/2008

  38. Summary and Conclusions • CDF Jet Energy Scale done in several steps • Tunes simulation and derives (part of the) corrections from MC • 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/Z-jet balance for cross-check and systematic uncertainties • 2-3% systematic uncertainty achieved depending on jet transverse energy • Can be reduced in specific measurement with in-situ calibrations (Wjj in lepton+jets top samples) • Approach for b-jet correction still rely on MC • Use of Z bbbar very promising For more details on JES at CDF arXiv:hep-ex/0510047, published on NIM 10.1016/j.nima.2006.05.269 http://www-cdf.fnal.gov/physics/new/top/2004/jets/cdfpublic.html Top Workshop@Grenoble, 10/23/2008

  39. Back-up

  40. Jet Algorithms Top Workshop@Grenoble, 10/23/2008

  41. Clusters using different Jet algorithms Top Workshop@Grenoble, 10/23/2008

  42. Details on GFlash (1) • Simulation of EM and Hadronic showers involves two steps • GFLASH calculated spatial distribution of energy, Edp, deposited by a shower w/in the calorimeter volume: depends on incident particle energy, shower fluctuations, sampling structure of detector • Fraction of deposited energy visible to active medium, is calculated. Depends on relative sampling fractions of MIPs, EM and Hadronic particles: Sem/Smip , Shad/Smip (tunable parameters) Longitudinal shower profiles modeled with Gamma-distribution. Hadronic showers classified 3-ways: ● Purely hadronic (h), scales w/absorption length, l0 ● Showers w/ p0 produced in 1st inelastic collision (f) ● Showers w/ p0 produced in later inelastic collision (l) 22 parameters total Top Workshop@Grenoble, 10/23/2008

  43. Details on GFlash (2) • Transverse/Lateral shower profiles for both EM and HAD particles are modeled with the Ansatz function: • R50 is given in units of Moliére Radius (RM) / Absorption Length (l0) for EM/HAD showers respectively. • The lateral spreading is taken to be linear (n=1) in HAD showers and quadratic (n=2) in EM showers.  14 parameters total Possible 38 parameters • 11 are tuned for central calorimeter • 7 are tuned for the plug (forward) calorimeter • Remaining parameters use default settings from H1 Collaboration • Relies on relative independence of shower profiles to particular calorimeter • Tuning in-situ w/ isolated track data: E/p measurements w/ isolated charged particles Top Workshop@Grenoble, 10/23/2008

  44. 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) Top Workshop@Grenoble, 10/23/2008

  45. 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! Top Workshop@Grenoble, 10/23/2008

  46. Corrections for FLAT input spectrum. Additional correction needed to unsmear (almost negligible, high granularity in dpT ) 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 Top Workshop@Grenoble, 10/23/2008

  47. 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 Top Workshop@Grenoble, 10/23/2008

  48. Uncertainties on OOC • The transverse energy around a jet of cone size Rjet is measured by adding the transverse energy in towers within the annulus defined by radii r1 and r2 around the jet axis Systematic uncertainty of OOC corrections for different cone sizes. The systematic uncertainty is taken as the largest difference between data and either PYTHIA or HERWIG Top Workshop@Grenoble, 10/23/2008

  49. 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%) Top Workshop@Grenoble, 10/23/2008

  50. D0 Calorimeter • LAr sampling • U absorber: Compensating •  linear response to hadrons • 7 interaction lengths • Same structure for barrel and plug • Resolutions: • EM energies (g,e): s/ET = 15%/√ET+0.3% • HAD energies(p±): s/ET = 45%/√ET+5% Top Workshop@Grenoble, 10/23/2008

More Related