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Top production and branching ratios

Top production and branching ratios. For DØ collaboration Elizaveta Shabalina University of Illinois at Chicago Wine and Cheese seminar at FNAL 09/16/05. Outline. Introduction Top pair production Dilepton channel Lepton+jets Event kinematics method B-tagging method

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Top production and branching ratios

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  1. Top production and branching ratios For DØ collaboration Elizaveta Shabalina University of Illinois at Chicago Wine and Cheese seminar at FNAL 09/16/05

  2. Outline • Introduction • Top pair production • Dilepton channel • Lepton+jets • Event kinematics method • B-tagging method • Top branching ratio • Top pair production in all hadronic channel E. Shabalina Joint Theoretical and Experimental Seminar

  3. Tevatron was built more than a decade ago to discover top quark successfully achieved in 1995 Run I cross sections: CDF and D0 Precision (~25%) was severely statistically limited At present s = 1.96 TeV - 30% higher production rate much higher luminosity Current goal – deliver precision measurements Theoretical prediction of cross section – 6.5% accuracy Tev2000 study: precision of ttbar cross section measurement Five W&C seminars since June 1st are dedicated to top physics Top quark physics today E. Shabalina Joint Theoretical and Experimental Seminar

  4. Important test of perturbative QCD Higher production rate ttbar resonances (topcolor) Measure in different channels Exotic top decays (to charged Higgs or light stop) different cross sections in different channels Dilepton to l+jets cross sections ratio tests top decays without W boson in final state Measure with different methods b-jet tagging method assumes Br (t  Wb) = 1 an implicit use of the SM prediction: |Vtb|=0.9990  0.9992 (at 90%C.L.) Topological method is free from this assumption Using both test of top decays without b quark in the final state Top cross section - motivation Test of Standard Model E. Shabalina Joint Theoretical and Experimental Seminar

  5. ~15% ~85% q-q g-g =1.98+0.22 pb -0.16 =0.88±0.04 pb Top production • Standard model pair production through strong interactions • Standard model electroweak production(single top) Discovered in Run I • = 6.77 ± 0.42 pb for mtop = 175 GeV To be observed in Run II E. Shabalina Joint Theoretical and Experimental Seminar

  6. Very short lifetime  decays as a free quark Br (t  Wb)  100% W decay modes determine top quark final state Dilepton (ee, μμ, eμ) Both W’s decay leptonically BR = 5% Lepton (e or μ) + jets One W decays leptonically, another one hadronically BR = 30% All-hadronic Both W’s decay hadronically BR = 44% τhad +X BR = 23% … and decay E. Shabalina Joint Theoretical and Experimental Seminar

  7. DØ detector All detector subsystems are important for high quality top quark measurements • Electrons- energy clusters in EM section of the calorimeter and track in the central tracking system • Muons - track segments in muon chambers and track in the central tracking system • Jets - clusters of energy in EM and hadronic parts of calorimeter E. Shabalina Joint Theoretical and Experimental Seminar

  8. Tuning simulation to data • Monte Carlo simulation is used to calculate selection efficiencies and to simulate event kinematics • improve the agreement between data and MC: • additional smearing of the reconstructed objects • correction factors derived from comparison of Zll data and MC events and applied to MC • Systematic uncertainties – from uncertainties on the smearing parameters and/or from the dependence on detector regions, various jet environment RMS SF = E. Shabalina Joint Theoretical and Experimental Seminar

  9. Electron Deposit >90% of energy in the EM calorimeter within a cone of R<0.2 relative to the shower axis Isolated: the ratio of the energy in the hollow cone 0.2 < R < 0.4 to the reconstructed electron energy ≤ 15% Transverse and longitudinal shower shapes consistent with those expected for an electron Reconstructed track found within R< 0.5 from the shower position in the calorimeter Discriminant combining information from central tracking system and calorimeter is consistent with the expectations for a high-pT isolated electron Muon a muon track segments are matched inside and outside of the toroid timing (from associated scintillator hits) is within 10 ns of the interaction  muon originates from primary vertex a track reconstructed in the tracking system belonging to event vertex is matched to the muon candidate found in the muon system Isolated in calorimeter and in the tracking system; isolation criteria are different for dilepton and l+jets analyses Electron and muon identification l o o s e l o o s e t i g h t t i g h t E. Shabalina Joint Theoretical and Experimental Seminar

  10. ET _  b  p b p E T  Dilepton channels • Signature • Selection • At least two jets (pT>20 GeV, |y|<2.5) • Two charged opposite sign leptons (pT>15 GeV; e: ||<1.1 or 1.5<||<2.5; μ: ||<2) • Lepton quality: “tight” μ, “tight” e in ee, “loose” e in eμ (electron discriminant distribution in data is used to extract ttbar signal) • Large missing ET in ee and μμchannels; no cut in eμ • and further selections are optimized for each channel to account for difference in backgrounds  p p  E. Shabalina Joint Theoretical and Experimental Seminar

  11. Drell-Yan background rejection ee: veto events with 80<Mee<100 >35 GeV(>40 GeV) for Mee>100 GeV (Mee<80 GeV) μμ: >35 GeV is tightened at low and high values of azimuthal distance φ( μ, ) Remove events with φ(μ, )>175° Physics Leptons from W/Z decay and missing ET from neutrinos: WW/WZ, Z/*ll Estimated from MC Instrumental jet or lepton in jet fakes isolated lepton (QCD, W+jets) missing ET originates from resolution effects, misreconstructed jet or lepton or noise in calorimeter (Drell-Yan processes Z/*ee(μμ) (eμ channel is not affected) ET ET ET ET ET Backgrounds E. Shabalina Joint Theoretical and Experimental Seminar

  12. Fake electron (W+jets, QCD events) Fake rate  from data sample dominated by fake electrons (2 loose EMs, low , outside Z mass window) Measure fraction of loose electrons that pass tight criteria Fake isolated muon (muons from heavy flavor decays) Use loose dimuon events One non-isolated muon Measure probability that the other is isolated Multiply by number of loose-tight events in data Fake in Z/* ee(μμ) – primary background in ee(μμ) channels spectrum in MC Z events agrees well with data μμ: directly from simulation ee: fake rate is measured in +jet events; multiplied by the number of data events that fail the selection but pass all others in MC 2 cut on fit to Z hypothesis (μμ) ET ET ET ET Backgrounds E. Shabalina Joint Theoretical and Experimental Seminar

  13. The cleanest channel Optimized to minimize total error Optimal cut removes Z/* background Extract fake electron background from the fit to the observed distribution of electron LH in data Shape for real electrons – from Z  ee data Shape for fake electrons – from background dominated sample (anti-isolated muon, low missing ET) eμ channel real electrons fake electrons E. Shabalina Joint Theoretical and Experimental Seminar

  14. Results Background control bin ttbar signal E. Shabalina Joint Theoretical and Experimental Seminar

  15. Dilepton events properties eμ Electron likelihood distribution for data events after full selection combined for tt= 7 pb E. Shabalina Joint Theoretical and Experimental Seminar

  16. Cross section calculation • ee and μμ channels – counting experiments • Define likelihood for each channel based on Poisson probability that expected number of signal + background events mj is compatible with observed Njobs • where • eμ channel – extended unbinned likelihood method - electron likelihood distributions for signal and background events - number of physics background events xi – value of electron likelihood for an electron in each event Fit simultaneously cross section and Nfake E. Shabalina Joint Theoretical and Experimental Seminar

  17. Cross sections ee: μμ: eμ: For dilepton channel combination minimize the sum of negative log-likelihood functions for individual channels 370 pb-1 combined dilepton @ m_top = 175 GeV E. Shabalina Joint Theoretical and Experimental Seminar

  18. Systematic uncertainties Comparable contributions from all sources E. Shabalina Joint Theoretical and Experimental Seminar

  19. Signature Selection One isolated lepton (pT>20 GeV; e: ||<1.1 or 1.5<||<2.5; μ: ||<2) At least four jets (pT>20 GeV, |y|<2.5) >20 GeV and not collinear with lepton direction in transverse plane ET b p E T Lepton+jets channel jet _  _  p b jet jet jet E. Shabalina Joint Theoretical and Experimental Seminar

  20. Sample composition Multijet background Estimate amount of QCD from Matrix Method Nloose Ntight E. Shabalina Joint Theoretical and Experimental Seminar

  21. – probability density functions for signal and background  a set of input variables normalized distributions of variable i for signal and background Discriminant function definition • Only ttbar and W+jets simulated events are used to build discriminant • Kinematic properties of multijet background are similar to W+jets Transform topological variables to be less sensitive to statistical fluctuations in regions of rapid variations Build logarithm of the signal to background ratios and fit with polinomial E. Shabalina Joint Theoretical and Experimental Seminar

  22. HT – scalar sum of the pT of four leading jets Centrality – ratio of scalar sum of jet pT to scalar sum of jet energies Aplanarity Sphericity Set of variables is chosen to provide the best separation between ttbar and W+jets background to have the least sensitivity to the dominant systematic uncertainties Only 4 highest pT jets are used to build variables ET (l,ET) Discriminating variables • kTmin=RjjminpTmin/ETW, Rjjmin  maximum separation between pairs of jets, ETW – scalar sum of lepton pT and , pTmin – pT of the lower pT jet Linear combination of the eigenvalues of a normalized momentum tensor E. Shabalina Joint Theoretical and Experimental Seminar

  23. Discriminant function • Fit modeled discriminant function distribution to that of data • Extract Nttbar, W+jets and multijet events in the sample By construction background peaks at 0, signal – at 1 E. Shabalina Joint Theoretical and Experimental Seminar

  24. Cross section • Define where Poisson probability density for n observed events given μi predicted, i runs over all bins of the discriminant, niobs – content of bin i as obtained in selected sample • Expected number of events in bin i is a function of number of ttbar, W and QCD events in the selected sample: • f - fractions in bin i of the ttbar, W and QCD discriminant templates • Second term implements Matrix Method constraint on number of QCD events via the Poisson probability of the observed number of events in loose but not tight sample E. Shabalina Joint Theoretical and Experimental Seminar

  25. Results 240 pb-1 e+jets μ+jets e+jets μ+jets E. Shabalina Joint Theoretical and Experimental Seminar

  26. Results combined For lepton+jets channel combination minimize the sum of negative log-likelihood functions for individual channels Sample composition: 38% ttbar 44% W+jets 18% multijet background 240 pb-1 combined @ m_top = 175 GeV E. Shabalina Joint Theoretical and Experimental Seminar

  27. Event kinematics signal dominated Background dominated E. Shabalina Joint Theoretical and Experimental Seminar

  28. Systematic uncertainties By far the largest systematic uncertainty comes from the Jet energy calibration, 90% of total error E. Shabalina Joint Theoretical and Experimental Seminar

  29. event has two b-jets b-jets in background processes are seldom Use this feature to discriminate signal from background Dramatically improves signal-to-background ratio Signature of a b decay is a displaced vertex Forms long lifetime of B-hadrons (c ~ 450μ) B-hadrons travel Lxy ~ 3mm before decay with large charged track multiplicity Use same selection as in topological analysis but Relax cut on jet transverse momentum: pT > 15 GeV Use events with njet3 Use events with one and two jets as control samples for background estimation Lepton+jets channel with b-tagging QCD W+jets E. Shabalina Joint Theoretical and Experimental Seminar

  30. Reconstructs secondary vertex 2 tracks with pT1GeV, 1 SMT hit, impact parameter significance >3.5 Removes tracks associated with K0S, 0 and photon conversions ( → e+e-) Positive tag: Secondary vertex within a jet with a decay length significance Lxy/Lxy>7 Negative tag: Secondary vertex within a jet with a decay length significance Lxy/Lxy<7 (due to resolution effects) b-tagging algorithm - SVT Impact parameter E. Shabalina Joint Theoretical and Experimental Seminar

  31. b-tagging efficiency Measured in dijet data events for jets with muon inside Compare two samples with different heavy flavor content (increased by tagging the away jet) Tag jets with two tagging algorithms SVT and SLT (SLT = soft muon with pTrel> 0.7 GeV inside a jet) Solve system of 8 eqs to extract semileptonic b-tagging efficiency Use MC to correct measured efficiency to that for inclusive b-decays Light tagging rate Measure negative tagging rate in dijet events (low missing ET) Correct for long-lived particles in light jets Heavy flavor contribution in dijet events c-tagging rate From MC corrected with the SF derived for b-tagging Tagging rates E. Shabalina Joint Theoretical and Experimental Seminar

  32. Backgrounds • Calculate QCD (non-W) contribution from Matrix Method • Subtract small backgrounds (single top, VV, Z) using known cross sections • Separate W from ttbar using difference in their tagging probability • Interpret excess in observed tagged events with 3 jets over predicted background as ttbar signal small bkgr E. Shabalina Joint Theoretical and Experimental Seminar

  33. Event tagging probability DØ RunII Preliminary, 363pb-1 • Use MC to calculate event tagging probability • Depends on the flavor composition of the jets in the final and on the overall event kinematics • Apply the tagging rates measured in data to each jet in MC based on its flavor, pT and y • For W+jets, use the ALPGEN MC to estimate the fraction of the different W+heavy flavor subprocesses. E. Shabalina Joint Theoretical and Experimental Seminar

  34. Results Backgrounddominated E. Shabalina Joint Theoretical and Experimental Seminar

  35. Kinematics of l+lets tagged sample DØ RunII Preliminary, 363pb-1 E. Shabalina Joint Theoretical and Experimental Seminar

  36. Cross section • Define likelihood based on Poisson probability that expected number of signal + background events mj is compatible with observed Njobs • The product is taken over 8 independent channels: e/μ +jets, one-/two-tags, 3rd and 4th jet multiplicity bins • Multijet background in each tagged sample, and the corresponding samples before tagging, is constrained within errors to the amount obtained from Matrix Method E. Shabalina Joint Theoretical and Experimental Seminar

  37. Result and systematic uncertainties • Combined statistical and systematic error is obtained • Individual contributions are obtained by refitting after fixing all but the Gaussian term under study • Gaussian term for each source of errors is included (nuisance parameter method) • Each source is allowed to affect the central value of the cross section 363 pb-1 • Systematic and statistical uncertainties are the same ~ 11% • Main sources: • JES and jet ID • B-tagging efficiency in data • W fractions • Luminosity E. Shabalina Joint Theoretical and Experimental Seminar

  38. Branching ratio • Probe the assumption Br(tWb)=1 • CKM matrix element |Vtb|=0.99900.9992 @90% C.L. R=0.99800.9984. True in SM assuming • Three quark generations • CKM matrix is unitary • For expanded CKM matrix |Vtb|=0.070.9993 @90% C.L. • CDF measurement: 162pb-1 E. Shabalina Joint Theoretical and Experimental Seminar

  39. Split selected sample into 3 categories: 0,1 and 2 tags Predicted number of ttbar events depends on R Fit R and tt from the number of observed tagged events and the event kinematics in 0 tag sample Method • Compute probabilities to observe 0, 1 and 2 tags for each final ttbar state • Combine to obtain Pn-tag(R), n-tag=0, 1, 2 • Use topological discriminant in 0 tag sample with 4 jets to determine ttbar content 2 b-jets 1 b, 1 light jet 2 light jets E. Shabalina Joint Theoretical and Experimental Seminar

  40. Fitting procedure • Perform binned maximum likelihood fit to data in • 10 bins of discriminant of l+jets 0 tag, Njet4 • 2 bins of l+jets 0 tag, Njet=3 • 4 bins of l+jets 1 tag, Njet=3, 4 • 4 bins of l+jets 2 tag, Njet=3, 4 • Statistical fluctuations of the multijet background are taken into account by additional 12 Poisson terms (0,1, 2 tags, nj=3, 4, e+jets, μ+jets) • Nuisance parameter method to include systematic uncertainties Njet=3 Njet4 E. Shabalina Joint Theoretical and Experimental Seminar

  41. 230 pb-1 Result Statistical uncertainty dominates Potential for improvement – include dilepton events E. Shabalina Joint Theoretical and Experimental Seminar

  42. Signature: 6 jets, 2 b-quark jets All decay products should be visible in the detector, no energetic neutrinos produced Six jet multijet production rate is many orders of magnitude larger than ttbar Impossible to extract signal without tagging b-jets – SVT algorithm is used Njets 6, pT>15 GeV Suppress multiple interactions (second interaction is also hard QCD process): Reject events with several hard primary vertices >3 cm apart At least 3 jets assigned Jet is assigned to PV if at least 2 tracks from it come from PV Removes 32% Reject bb background: R(tagged jets)>1.5 All hadronic channel E. Shabalina Joint Theoretical and Experimental Seminar

  43. Derive TRF (tag rate function) in the 6-jet data sample (ttbar contribution is ~0.3%) in 4 bins of HT: 0200, 200  300, 300  400, 400 GeV Parameterize as a function of jet pT, γ, φ, position of primary vertex along the beam Compare predicted and observed tagging rates and obtain correction factor Select a set of variables discriminating signal from background Avoid as much as possible JES dependent variable Use smallest possible number of input variables Combine into Neural network TRF and neural network E. Shabalina Joint Theoretical and Experimental Seminar

  44. Discriminating variables • Energy Scale – HT • Event Shape – aplanarity • Soft non-leading Jets – ET56 – geometric mean of the transverse energies of the 5th and 6th leading jet Variables are designed to address different aspects of the background • Rapidity – <h2> - weighted RMS of h of 6 leading jets • Top Properties – • Mmin3,4 – the second smallest dijet mass • Mass likelihood, 2-like variable calculated from MW, W, top E. Shabalina Joint Theoretical and Experimental Seminar

  45. Background was estimated on the sample containing signal correct cross section TRF – probability to tag ttbar MC event using TRF btag – probability to tag ttbar event using b,c and light tagging rates Nobs = 541 NTRF = 494±8 nn>0.9 Cross section calculation E. Shabalina Joint Theoretical and Experimental Seminar

  46. Cross section and uncertainties At mtop = 175 GeV, 350 pb-1 ~55% relative error Potential for improvement: make better use of double tagged events JES error dominates – 70% of total systematic error CDF 311 pb-1 : ~40% relative error E. Shabalina Joint Theoretical and Experimental Seminar

  47. Summary Accepted for publication in PLB Best precision: ~16% l+jets/btag at 363 pb-1 Work in progress on combination of the latest results up to 370 pb-1 CDF combined up to 350 pb-1 ~13% relative error E. Shabalina Joint Theoretical and Experimental Seminar

  48. Do we meet expectations? For 363 pb-1: predicted – 180 b-tagged events (scaled from 500 per fb-1) Observed – 140 (241 tagged event, 101 – expected background) Can we do better? Data quality Improved calorimeter calibration Improved performance of SMT is crucial Improved simulation Optimization Better tools Neural network lifetime b-tagger Fighting major sources of systematic uncertainties From TeV2000 to reality E. Shabalina Joint Theoretical and Experimental Seminar

  49. Glance into the future Total error on l+jets/btag channel • Assumptions: • Errors from limited MC statistics are set to 0 • Luminosity dependent and constant terms: • JES • B-tagging efficiency • Lepton identification • Limiting factors: • Luminosity (6.5%) • Heavy flavor fractions (5.9%) • Solutions: • Measure ratio of ttbar to W+jets cross section • Combine channels This will be replaced by a real plot E. Shabalina Joint Theoretical and Experimental Seminar

  50. Conclusion • The precision of the latest top pair production cross section measurements rapidly approaches accuracy of theoretical prediction and will allow to probe Standard Model • With combination of measurements in different channels and using different methods we have an excellent opportunity to exceed the precision limit set by TeV2000 – 11% for 1 fb-1 • … and the one for 10 fb-1 – 5.9% – but with less luminosity! This is a challenge. Let’s go for it! E. Shabalina Joint Theoretical and Experimental Seminar

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