Top pair production cross section and branching ratio measurements

1. Top pair production cross section and branching ratio measurements For the DØCollaboration Elizaveta Shabalina University of Illinois at Chicago Joint Theoretical and Experimental Seminar Fermilab, 09/16/05

2. Outline • Introduction • Top pair production • One lepton and jets • Event kinematics method • b-jet tagging method • Br (tWb)/Br (tWq) • Top pair production • all hadronic • two leptons and jets • Conclusions E. Shabalina Joint Theoretical and Experimental Seminar

3. The biggest accomplishment of Run I of Tevatron was the top quark discovery in 1995 Run I cross sections with >100 pb-1: CDF D0 Precision (~25%) was severely limited by statistics 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: projected precision of ttbar cross section measurement Five W&C seminars since June 1st were dedicated to top physics Top quark physics today E. Shabalina Joint Theoretical and Experimental Seminar

4. Important test of perturbative QCD Higher production rate e. g. ttbar resonances Measure in different decay channels Exotic top decays (to charged Higgs or light stop) different cross sections in different channels Dilepton to l+jets cross sections ratio probes non-W boson top decays Measure with different methods b-jet tagging method normally 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 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 via the strong interaction • 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 Top pair production is one of the main backgrounds to single top 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 = 6% Lepton (e or μ) + jets One W decays leptonically, another one hadronically BR = 34% All-hadronic Both W’s decay hadronically BR = 46% τhad +X BR = 14% … and decay Lepton+jets Lepton+jets 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 • Jet tagging requires tracks E. Shabalina Joint Theoretical and Experimental Seminar

8. Electron High fraction of energy in the EM calorimeter Isolated in calorimeter Transverse and longitudinal shower shapes consistent with those expected for an electron Matched central track High value of electron discriminant (tracking and calorimeter information combined) Muon segments in muon system matched inside and outside of the toroid Non-cosmic (based on timing from associated scintillator hits) Matched central track Isolated in calorimeter and in the tracking system Electron and muon identification loose loose tight tight Loose and tight lepton quality is used to determine backgrounds E. Shabalina Joint Theoretical and Experimental Seminar

9. Signature Selection One isolated lepton (pT>20 GeV; e: ||<1.1, μ: ||<2) At least four jets (pT>20 GeV, |y|<2.5) >20 GeV and not collinear with lepton direction in transverse plane Features: Relatively high Br Manageable background Perfect for studies of top properties ET b p E T Lepton+jets channel jet _  _  p b jet jet jet E. Shabalina Joint Theoretical and Experimental Seminar

10. multijet background from Matrix Method W+jets W+jets Multijet ttbar ttbar Sample composition W+jets Triggered data Multijet background ttbar Loose leptonic W selection + 4 jets Tight leptonic W selection + 4 jets Multijet Loose to tight lepton Ntight Nloose Combine topological event information into a discriminant and perform fit to the data ttbar E. Shabalina Joint Theoretical and Experimental Seminar

11. (l, ET) Discriminating variables Provide the best separation between ttbar and W+jets and the least sensitivity to the dominant systematics • Energetic  HT • Central • Centrality: HT/H • Spherical • Aplanarity (large A spherical events) • Sphericity (large S isotropic events) • kTmin – measure of minimum jet pT relative to another • Use only 4 highest pT jets Top events are E. Shabalina Joint Theoretical and Experimental Seminar

12. – probability density functions for signal and background  a set of input variables Discriminant function For uncorrelated variables • Kinematic properties of multijet background are similar to W+jets after selection • Use only ttbar and W+jets to build discriminant • Extract Nttbar, W+jets and multijet events in the sample from fit to discriminant distribution in data E. Shabalina Joint Theoretical and Experimental Seminar

13. 240 pb-1 e+jets μ+jets e+jets μ+jets Results of the fit E. Shabalina Joint Theoretical and Experimental Seminar

14. Results combined For lepton+jets channel combination minimize the sum of negative log-likelihood functions for individual channels 240 pb-1 Sample composition: 38% ttbar 44% W+jets 18% multijet Statistical and systematic uncertainties are comparable hep-ex/0504043 combined @ m_top = 175 GeV E. Shabalina Joint Theoretical and Experimental Seminar

15. Event kinematics background dominated signal dominated D<0.5 D>0.5 E. Shabalina Joint Theoretical and Experimental Seminar

16. Systematic uncertainties tt(pb) By far the largest systematic uncertainty comes from the Jet energy calibration, 90% of total error E. Shabalina Joint Theoretical and Experimental Seminar

17. event has 2 b-jets b-jets in background processes are rare Use this feature to discriminate signal from background Dramatically improves signal-to-background ratio 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 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 ttbar Lepton+jets channel with b-tagging b-tagging W+jets ttbar E. Shabalina Joint Theoretical and Experimental Seminar

18. Reconstructs secondary vertex 2 tracks with pT1GeV, 1 SMT hit, impact parameter significance >3.5 Remove 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 K0S E. Shabalina Joint Theoretical and Experimental Seminar

19. b-tagging efficiency Measured in dijet data events for jets with muon inside Compare two samples with different heavy flavor content 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 in bins of jet pT and y to extract semileptonic b-tagging efficiency Use MC to correct measured efficiency to inclusive one 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

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

21. Event tagging probability • For W+jets, use the ALPGEN MC to estimate the fraction of the different W+heavy flavor subprocesses • Limited knowledge of heavy flavor fractions is one of the largest sources of systematics • Use MC to calculate event tagging probability • Rely only on the flavor composition of the jets in the final state and overall event kinematics • Apply the tagging rates measured in data to each jet in MC based on its flavor, pT and y E. Shabalina Joint Theoretical and Experimental Seminar

22. Results DØ RunII Preliminary, 363pb-1 for tt= 7 pb 1 tag 2 tags Background dominated E. Shabalina Joint Theoretical and Experimental Seminar

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

24. Result and systematic uncertainties • Results is combined statistical and systematic error • Refitting after fixing all but one Gaussian term to obtain error from one source • Perform fit in 8 channels • Gaussian term for each source of errors (nuisance parameter method) • Each source affects 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

25. Probing the assumption Br(tWb)=1 • q = b, s or d-quark • CKM matrix element |Vtb|=0.9990 to 0.9992 @90% C.L. R=0.9980 to 0.9984. True in SM assuming three generations of quarks • For expanded CKM matrix |Vtb|=0.070.9993 @90% C.L. • Measure of R to test SM • CDF measurement: 162 pb-1 E. Shabalina Joint Theoretical and Experimental Seminar

26. 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 light quark E. Shabalina Joint Theoretical and Experimental Seminar

27. 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 • Nuisance parameter method to include systematic uncertainties (similar to l+jets/btag analysis) Njet=3 Njet4 Br(tWb)=1 and tt=7 pb E. Shabalina Joint Theoretical and Experimental Seminar

28. 230 pb-1 Result The most precise measurement to date Model independent measurement • Potential for improvement • include dilepton events • higher b-tagging efficiency Statistical uncertainty dominates: 90% of total error on R E. Shabalina Joint Theoretical and Experimental Seminar

29. 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 Selection Njets 6, pT>15 GeV Suppress multiple interactions: Reject events with >1 hard primary vertices >3 cm apart At least 3 jets assigned Jet is assigned to PV if 2 tracks from it come from PV Removes 32% Reject bb background: R(tagged jets)>1.5 Selected sample contains ~0.3% of ttbar All hadronic channel E. Shabalina Joint Theoretical and Experimental Seminar

30. Use untagged selected sample Apply TRF (tag rate function) to predict background after tagging Derive TRF in selected sample 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 Select variables discriminating signal from background Avoid JES dependent variables Use the smallest possible number of input variables Optimized for the smallest systematic error Combine into Neural Network (NN) Background estimate TRF 300<HT<400 3% E. Shabalina Joint Theoretical and Experimental Seminar

31. Discriminating variables NN • 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

32. 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 Signal region E. Shabalina Joint Theoretical and Experimental Seminar

33. Result At mtop = 175 GeV, 350 pb-1 New measurement Potential for improvement: optimize use of double tagged events JES error dominates – 70% of total systematic error E. Shabalina Joint Theoretical and Experimental Seminar

34. _  b  p b p E T  Dilepton channels  p p Presented at W&C seminar on July 8th  Small branching fraction, small backgrounds E. Shabalina Joint Theoretical and Experimental Seminar

35. ET Dilepton channels • 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) • and further selections are optimized for each channel to account for difference in backgrounds in resolution • Physics backgrounds • Leptons from W/Z decay and missing ET from neutrinos: WW/WZ, Z/*ll • Estimated from MC • Instrumental backgrounds • 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) • Estimated from data  p E. Shabalina Joint Theoretical and Experimental Seminar

36. 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

37. Summary of channels E. Shabalina Joint Theoretical and Experimental Seminar

38. Combined result ttbar signal Background control bin 370 pb-1 Signal significance – 4.8 combined dilepton @ m_top = 175 GeV E. Shabalina Joint Theoretical and Experimental Seminar

39. Systematic uncertainties tt(pb) Comparable contributions from all sources Still statistic error dominates: ~25%; systematics: ~12% E. Shabalina Joint Theoretical and Experimental Seminar

40. Summary hep-ex/0505082, 230 pb-1 Accepted for publication in PLB hep-ex/0504058, 230 pb-1 Best precision: ~16% l+jets/btag at 363 pb-1 Work in progress on combination of the latest results up to 370 pb-1 • What improvement do we expect from combination? • CDF best result – l+jets/btag – ~16% total error (318 pb-1) • CDF combined up to 350 pb-1 ~13% relative error E. Shabalina Joint Theoretical and Experimental Seminar

41. 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) We are close to the expected performance Can we do better? Data quality Improved calorimeter calibration Improved performance of SMT is crucial (Layer 0 to be installed during the shutdown) Improved simulation Increase acceptance Better tools Neural network lifetime b-tagger is almost ready Fighting major sources of systematic uncertainties From TeV2000 to reality E. Shabalina Joint Theoretical and Experimental Seminar

42. Glance into the future Total projected error / exp on l+jets/btag channel • Assumptions: • Zero errors from limited MC statistics • Luminosity dependent and constant terms: • JES • b-tagging efficiency • Lepton identification • Limiting factors: • Luminosity (6.5%) • Heavy flavor fractions (6%) • Solutions: • Luminosity from W’s (2-3%) • Measure ratio of ttbar to W cross section • Use large data sets to constrain model assumptions (W fractions, gluon radiation, …) • Combine channels 363 pb-1 E. Shabalina Joint Theoretical and Experimental Seminar

43. Conclusion • The precision of the latest top pair production cross section measurements rapidly approaches accuracy of theoretical prediction and will allow to sensitively probe the Standard Model • With combination of measurements in different channels and using different methods we have an excellent opportunity to exceed the precision goal set by TeV2000 – 11% for 1 fb-1 • … and the one for 10 fb-1 – 6% – but with less luminosity! This is a challenge but we are on track to make it E. Shabalina Joint Theoretical and Experimental Seminar