1. 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. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 2 Outline Introduction Introduction Top pair production Top pair production –Dilepton channel –Lepton+jets Event kinematics method Event kinematics method B-tagging method B-tagging method Top branching ratio Top branching ratio Top pair production in all hadronic channel Top pair production in all hadronic channel

3. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 3 Tevatron was built more than a decade ago to discover top quark successfully achieved in 1995 Tevatron was built more than a decade ago to discover top quark successfully achieved in 1995 Run I cross sections: Run I cross sections: –CDF and D0 Precision (~25%) was severely statistically limited Precision (~25%) was severely statistically limited Top quark physics today At present At present –  s = 1.96 TeV - 30% higher production rate –much higher luminosity Current goal – deliver precision measurements Current goal – deliver precision measurements Theoretical prediction of cross section – 6.5% accuracy Theoretical prediction of cross section – 6.5% accuracy Tev2000 study: precision of ttbar cross section measurement Tev2000 study: precision of ttbar cross section measurement Five W&C seminars since June 1 st are dedicated to top physics Five W&C seminars since June 1 st are dedicated to top physics 1 fb -1 11% 10 fb -1 5.9%

4. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 4 Top cross section - motivation Important test of perturbative QCD Important test of perturbative QCD Higher production rate ttbar resonances (topcolor) Higher production rate ttbar resonances (topcolor) Measure in different channels 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 Measure with different methods –b-jet tagging method assumes Br (t  Wb) = 1 an implicit use of the SM prediction: |V tb |=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 Test of Standard Model

5. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 5 Top production Standard model pair production through strong interactions Standard model pair production through strong interactions Standard model electroweak production (single top) Standard model electroweak production (single top) q-q ~85% ~15% g-g Discovered in Run I To be observed in Run II  = 6.77 ± 0.42 pb for m top = 175 GeV  =0.88±0.04 pb  =1.98 +0.22 pb -0.16

6. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 6 Very short lifetime  decays as a free quark Very short lifetime  decays as a free quark Br (t  Wb)  100% Br (t  Wb)  100% … and decay W decay modes determine top quark final state W decay modes determine top quark final state Dilepton (ee, μμ, eμ) Dilepton (ee, μμ, eμ) –Both W’s decay leptonically –BR = 5% Lepton (e or μ) + jets Lepton (e or μ) + jets –One W decays leptonically, another one hadronically –BR = 30% All-hadronic All-hadronic –Both W’s decay hadronically –BR = 44% τ had +X τ had +X –BR = 23%

7. 09/16/05 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 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 Muons - track segments in muon chambers and track in the central tracking system Jets - clusters of energy in EM and hadronic parts of calorimeter Jets - clusters of energy in EM and hadronic parts of calorimeter

8. 09/16/05 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 Monte Carlo simulation is used to calculate selection efficiencies and to simulate event kinematics improve the agreement between data and MC: 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 Systematic uncertainties – from uncertainties on the smearing parameters and/or from the dependence on detector regions, various jet environment SF = RMS

9. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 9 Electron Deposit >90% of energy in the EM calorimeter within a cone of  R 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% 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 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 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-p T isolated electron Discriminant combining information from central tracking system and calorimeter is consistent with the expectations for a high-p T isolated electron Electron and muon identification Muon a muon track segments are matched inside and outside of the toroid 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 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 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 Isolated in calorimeter and in the tracking system; isolation criteria are different for dilepton and l+jets analyses l o o s e t i g h t l o o s e t i g h t

10. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 10 b b p p E T Dilepton channels Selection Selection –At least two jets (p T >20 GeV, |y| 20 GeV, |y|<2.5) –Two charged opposite sign leptons (p T >15 GeV; e: |  | 15 GeV; e: |  |<1.1 or 1.5<|  |<2.5; μ : |  |<2) –Lepton quality: “tight” μ, “tight” e in ee, “loose” e in e μ ( electron discriminant distribution in data is used to extract ttbar signal) –Large missing E T in ee and μμ channels; no cut in e μ and further selections are optimized for each channel to account for difference in backgrounds and further selections are optimized for each channel to account for difference in backgrounds Signature Signature _ ETETETET p p 

11. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 11 Drell-Yan background rejection Drell-Yan background rejection ee: ee: –veto events with 80<M ee <100 – >35 GeV(>40 GeV) for M ee >100 GeV (M ee <80 GeV) μμ: μμ: – >35 GeV – is tightened at low and high values of azimuthal distance  φ( μ, ) –Remove events with  φ( μ, )>175° Backgrounds Physics Physics –Leptons from W/Z decay and missing E T from neutrinos: WW/WZ, Z/  *  ll –Estimated from MC Instrumental Instrumental –jet or lepton in jet fakes isolated lepton (QCD, W+jets) –missing E T originates from resolution effects, misreconstructed jet or lepton or noise in calorimeter (Drell-Yan processes Z/  *  ee( μμ ) (e μ channel is not affected) ETETETET ETETETET ETETETET ETETETET ETETETET

12. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 12 Backgrounds Fake electron (W+jets, QCD events) 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) 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 Multiply by number of loose-tight events in data Fake in Z/  *  ee(μμ) – primary background in ee(μμ) channels 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)  2 cut on fit to Z hypothesis (μμ) ETETETET ETETETET ETETETET ETETETET

13. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 13 e μ channel The cleanest channel The cleanest channel Optimized to minimize total error Optimized to minimize total error Optimal cut removes Z/  *  background Optimal cut removes Z/  *  background Extract fake electron background from the fit to the observed distribution of electron LH in data 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 real electrons – from Z  ee data Shape for fake electrons – from background dominated sample (anti-isolated muon, low missing E T ) Shape for fake electrons – from background dominated sample (anti-isolated muon, low missing E T ) real electrons fake electrons

14. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 14 Results Background control bin ttbar signal

15. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 15 Dilepton events properties Electron likelihood distribution for data events after full selection eμeμ combined for  tt = 7 pb

16. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 16 ee and ee and μμ channels – counting experiments – –Define likelihood for each channel based on Poisson probability that expected number of signal + background events  j is compatible with observed N j obs – –where eμ channel – extended unbinned likelihood method Cross section calculation - electron likelihood distributions for signal and background events x i – value of electron likelihood for an electron in each event - number of physics background events Fit simultaneously cross section and N fake

17. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 17 ee: Cross sections μμ : eμ:eμ: For dilepton channel combination minimize the sum of negative log- likelihood functions for individual channels combined dilepton @ m_top = 175 GeV 370 pb -1

18. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 18 Systematic uncertainties Comparable contributions from all sources

19. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 19 Lepton+jets channel Signature Signature Selection Selection –One isolated lepton (p T >20 GeV; e: |  |<1.1 or 1.5<|  |<2.5; μ : |  |<2) –At least four jets (p T >20 GeV, |y|<2.5) – >20 GeV and not collinear with lepton direction in transverse plane b b p p E T jet _ _  ETETETET

20. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 20 Sample composition Estimate amount of QCD from Matrix Method Multijet background N loose N tight

21. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 21 Discriminant function definition – probability density functions for signal and background  a set of input variables  normalized distributions of variable i for signal and background 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 Only ttbar and W+jets simulated events are used to build discriminant Kinematic properties of multijet background are similar to W+jets

22. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 22 Discriminating variables H T – scalar sum of the p T of four leading jets H T – scalar sum of the p T of four leading jets Centrality – ratio of scalar sum of jet p T to scalar sum of jet energies Centrality – ratio of scalar sum of jet p T to scalar sum of jet energies Aplanarity Aplanarity Sphericity Sphericity Set of variables is chosen 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 p T jets are used to build variables Only 4 highest p T jets are used to build variables  (l,E T ) k Tmin =  R jj min p T min /E T W,  R jj min  maximum separation between pairs of jets, E T W – scalar sum of lepton p T and, p T min – p T of the lower p T jet k Tmin =  R jj min p T min /E T W,  R jj min  maximum separation between pairs of jets, E T W – scalar sum of lepton p T and, p T min – p T of the lower p T jet ETETETET Linear combination of the eigenvalues of a normalized momentum tensor

23. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 23 By construction background peaks at 0, signal – at 1 Discriminant function Fit modeled discriminant function distribution to that of data Extract N ttbar, W+jets and multijet events in the sample

24. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 24 Cross section Define Define where Poisson probability density for n observed events given μ i predicted, i runs over all bins of the discriminant, n i obs – content of bin i as obtained in selected sample where Poisson probability density for n observed events given μ i predicted, i runs over all bins of the discriminant, n i obs – 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: 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 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 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

25. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 25 Results e+jets μ +jets e+jets μ +jets 240 pb -1

26. 09/16/05 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 combined @ m_top = 175 GeV Sample composition: 38% ttbar 44% W+jets 18% multijet background 240 pb -1

27. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 27 Event kinematics Background dominated signal dominated

28. 09/16/05 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

29. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 29 event has two b-jets event has two b-jets b-jets in background processes are seldom b-jets in background processes are seldom Use this feature to discriminate signal from background Use this feature to discriminate signal from background Dramatically improves signal- to-background ratio Dramatically improves signal- to-background ratio Signature of a b decay is a displaced vertex –Forms long –Forms long lifetime of B- hadrons (c  ~ 450μ) – –B-hadrons travel L xy ~ 3mm before decay with large charged track multiplicity Lepton+jets channel with b-tagging Use same selection as in topological analysis but –Relax cut on jet transverse momentum: p T > 15 GeV –Use events with n jet  3 Use events with one and two jets as control samples for background estimation QCD W+jets

30. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 30 Reconstructs secondary vertex – –  2 tracks with p T  1GeV,  1 SMT hit, impact parameter significance >3.5 Removes tracks associated with K 0 S,  0 and photon conversions (  → e + e - ) Positive tag: – –Secondary vertex within a jet with a decay length significance L xy /  Lxy >7 Negative tag: – –Secondary vertex within a jet with a decay length significance L xy /  Lxy <  7 (due to resolution effects) b-tagging algorithm - SVT Impact parameter

31. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 31 Tagging rates b-tagging efficiency 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 p T rel > 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 Light tagging rate –Measure negative tagging rate in dijet events (low missing E T ) –Correct for long-lived particles in light jets –Heavy flavor contribution in dijet events c-tagging rate c-tagging rate –From MC corrected with the SF derived for b-tagging

32. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 32 Backgrounds Calculate QCD (non- W) contribution from Matrix Method Calculate QCD (non- W) contribution from Matrix Method Subtract small backgrounds (single top, VV, Z  ) using known cross sections Subtract small backgrounds (single top, VV, Z  ) using known cross sections Separate W from ttbar using difference in their tagging probability 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 Interpret excess in observed tagged events with  3 jets over predicted background as ttbar signal small bkgr

33. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 33 Event tagging probability DØ RunII Preliminary, 363pb -1 W+jets, average ttbar ≥4j, 1 tag 4%44% ≥4j, 2 tag 0.4%15% Use MC to calculate event tagging probability Use MC to calculate event tagging probability Depends on the flavor composition of the jets in the final and on the overall event kinematics 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, p and y Apply the tagging rates measured in data to each jet in MC based on its flavor, p T and y For W+jets, use the ALPGEN MC to estimate the fraction of the different W+heavy flavor subprocesses. For W+jets, use the ALPGEN MC to estimate the fraction of the different W+heavy flavor subprocesses.

34. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 34 Results ≥3j, 1tag ≥3j, 2tag ≥4j, 1tag ≥4j, 2tag Expect bkg 71 ± 9 7 ± 1 22 ± 3 1.5±0.3 S/B0.61.62.312 Observed tags 121118821 Background dominated

35. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 35 Kinematics of l+lets tagged sample DØ RunII Preliminary, 363pb -1

36. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 36 Cross section Define likelihood based on Poisson probability that expected number of signal + background events  j is compatible with observed N j obs The product is taken over 8 independent channels: e/ μ +jets, one-/two-tags, 3 rd and 4 th 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

37. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 37 Result and systematic uncertainties Gaussian term for each source of errors is included (nuisance parameter method) 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 Each source is allowed to affect the central value of the cross section Systematic and statistical uncertainties are the same ~ 11% Main sources: JES and jet ID B-tagging efficiency in data W fractions Luminosity Combined statistical and systematic error is obtained Combined statistical and systematic error is obtained Individual Individual contributions are obtained by refitting after fixing all but the Gaussian term under study 363 pb -1

38. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 38 Probe the assumption Br(t  Wb)=1 Probe the assumption Br(t  Wb)=1 CKM matrix element |V tb |=0.9990  0.9992 @90% C.L. CKM matrix element |V tb |=0.9990  0.9992 @90% C.L. R=0.9980  0.9984. True in SM assuming R=0.9980  0.9984. True in SM assuming –Three quark generations –CKM matrix is unitary For expanded CKM matrix |V tb |=0.07  0.9993 @90% C.L. For expanded CKM matrix |V tb |=0.07  0.9993 @90% C.L. CDF measurement: 162pb -1 CDF measurement: 162pb -1 Branching ratio

39. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 39 Method 2 b-jets 1 b, 1 light jet 2 light jets Split selected sample into 3 categories: 0,1 and  2 tags Split selected sample into 3 categories: 0,1 and  2 tags Predicted number of ttbar events depends on R 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 Fit R and  tt from the number of observed tagged events and the event kinematics in 0 tag sample Compute probabilities to observe 0, 1 and  2 tags for each final ttbar state Compute probabilities to observe 0, 1 and  2 tags for each final ttbar state Combine to obtain Combine to obtain P n-tag (R), n-tag=0, 1,  2 P n-tag (R), n-tag=0, 1,  2 Use topological discriminant in 0 tag sample with  4 jets to determine ttbar content Use topological discriminant in 0 tag sample with  4 jets to determine ttbar content

40. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 40 Fitting procedure Perform binned maximum likelihood fit to data in Perform binned maximum likelihood fit to data in –10 bins of discriminant of l+jets 0 tag, N jet  4 –2 bins of l+jets 0 tag, N jet =3 –4 bins of l+jets 1 tag, N jet =3,  4 –4 bins of l+jets 2 tag, N jet =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 Nuisance parameter method to include systematic uncertainties N jet =3 N jet  4

41. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 41 Result Statistical uncertainty dominates 230 pb -1 Potential for improvement – include dilepton events

42. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 42 All hadronic channel Signature: 6 jets, 2 b- quark jets Signature: 6 jets, 2 b- quark jets All decay products should be visible in the detector, no energetic neutrinos produced 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 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 Impossible to extract signal without tagging b- jets – SVT algorithm is used N jets  6, p T >15 GeV N jets  6, p T >15 GeV Suppress multiple interactions (second interaction is also hard QCD process): 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: Reject bb background: –  R(tagged jets)>1.5

43. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 43 TRF and neural network Derive TRF (tag rate function) in the 6-jet data sample (ttbar contribution is ~0.3%) in 4 bins of H T : 0  200, 200  300, 300  400,  400 GeV Derive TRF (tag rate function) in the 6-jet data sample (ttbar contribution is ~0.3%) in 4 bins of H T : 0  200, 200  300, 300  400,  400 GeV Parameterize as a function of jet p T, γ, φ, position of primary vertex along the beam Parameterize as a function of jet p T, γ, φ, position of primary vertex along the beam Compare predicted and observed tagging rates and obtain correction factor Compare predicted and observed tagging rates and obtain correction factor Select a set of variables discriminating signal from background 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 Combine into Neural network

44. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 44 Discriminating variables Energy Scale – H T Energy Scale – H T Event Shape – aplanarity Event Shape – aplanarity Soft non-leading Jets – E T56 – geometric mean of the transverse energies of the 5 th and 6 th leading jet Soft non-leading Jets – E T56 – geometric mean of the transverse energies of the 5 th and 6 th leading jet Variables are designed to address different aspects of the background Rapidity – - weighted RMS of  of 6 leading jets Rapidity – - weighted RMS of  of 6 leading jets Top Properties – Top Properties – –M min 3,4 – the second smallest dijet mass –Mass likelihood,  2 -like variable calculated from M W,  W,  top

45. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 45 Cross section calculation Background was estimated on the sample containing signal correct cross section Background was estimated on the sample containing signal correct cross section  TRF  TRF – probability to tag ttbar MC event using TRF  btag – probability to tag ttbar event using b,c and light tagging rates  btag – probability to tag ttbar event using b,c and light tagging rates  TRF 0.125±0.002  btag 0.60±0.01 0.60±0.01  TRF  TRF  TRF /  TRF0.207±0.004 N obs = 541 N TRF = 494±8 nn>0.9

46. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 46 Cross section and uncertainties At m top = 175 GeV, 350 pb -1 JES error dominates – 70% of total systematic error CDF 311 pb -1 : ~40% relative error ~55% relative error Potential for improvement: make better use of double tagged events

47. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 47 Summary CDF combined up to 350 pb -1 ~13% relative error Accepted for publication in PLB Work in progress on combination of the latest results up to 370 pb -1 Best precision: ~16% l+jets/btag at 363 pb -1

48. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 48 From TeV2000 to reality Do we meet expectations? Do we meet expectations? For 363 pb -1 : 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? 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

49. 09/16/05 E. Shabalina Joint Theoretical and Experimental Seminar 49 Glance into the future Assumptions: Assumptions: –Errors from limited MC statistics are set to 0 –Luminosity dependent and constant terms: JES JES B-tagging efficiency B-tagging efficiency Lepton identification Lepton identification Limiting factors: Limiting factors: –Luminosity (6.5%) –Heavy flavor fractions (5.9%) Solutions: Solutions: –Measure ratio of ttbar to W+jets cross section –Combine channels This will be replaced by a real plot Total error on l+jets/btag channel

50. 09/16/05 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 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 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! … and the one for 10 fb -1 – 5.9% – but with less luminosity! This is a challenge. Let’s go for it!