Tobias Golling - SCIPP - August 24th 2004

Transcription

1 Measurement of the Top Quark Pair Production Cross Section using a Topological Likelihood and using Lifetime b-tagging with the DØ Detector in Run II at the Fermilab Tevatron at a center of mass energy of s = 1.96 TeV - SCIPP - August 24th 2004 page 1

2 Overview µ b q W+ p t p t Wq' µ Physics Motivation Tevatron & DØ Top Quark Production and Decay Overview over the Analyses Methods b 1) Common W+Jets Preselection 2a) Topological Analysis 2b) Lifetime b-tagging Analysis Summary & Outlook page 2

3 The Top Quark in the Standard Model Why is the Top Quark so interesting? completes the quark sector large mass mtop ~ 180 GeV / c2 short lifetime ~ s sensitive to physics beyond the Standard Model Discovery of the Top Quark in 1995 by the CDF and DØ Collaborations. Top-antitop production cross section test of perturbative QCD sensitive to top decays to New Physics particles (e.g. charged Higgs) Physics beyond the SM decaying to top-antitop Higgs-Boson coupling to fermions: f ~ mf t ~ 1 page 3

4 The Top Quark in the Standard Model Corrections to W and Z boson mass from top quark and Higgs boson loops allow prediction of the top quark and the Higgs boson mass zero order 1 Comparison of precision EW measurement corrections prediction of top quark mass observation discovery page 4

5 The Top Quark in the Standard Model corrections to W and Z boson mass from top quark and Higgs boson loops allow prediction of the top quark and the Higgs boson mass corrections: ~ Mt2 corrections: ~ ln (MH) Goal for Tevatron in Run II: Mt MW = = 3 GeV 20 MeV page 5

6 The Tevatron at Fermilab Chicago CDF Booster Tevatron _ p source DØ p _ p Main Injector & Recycler p p s =1.96 TeV t = 396 ns Only place to study the top quark before the LHC era Run I Run II (?) Lint ~ 125 pb-1 40 larger dataset at increased energy page 6

7 DØ Detector at Fermilab new silicon and fibre tracker new ~2 T solenoid upgraded muon system upgraded trigger/daq page 7

8 The Silicon Microstrip Tracker Silicon Microstrip Tracker (SMT): 6 barrels, 16 disks tracking out to ~ = 3 axial, double-sided small-angle stereo and double-sided 90 detectors 800k channels, SVX2 readout page 8

9 Data Set analysed data N = σ L dt installation finished and detector commissioning Analysed data set in Run I ( ): ~ 125 pb -1 Analysed data set in Run II (08/ /2003) for winter and summer '04 conferences: ~ pb -1 Data set in Run II until 2009: ~ 4-9 fb -1 Run I record page 9

10 Top Quark Production Top quarks mainly produced in pairs at Tevatron and LHC qq gg Run I: ttbar=5.7±1.6 pb (statistics limited) σtt ttbarnlo (pb) qq->tt gg->tt Run I (1.8 TeV) 4.87±10% 90% 10% Run II (2.0 TeV) 6.70±10% 85% 15% LHC (14 TeV) 803±15% 10% 90% typical S/B: Tevatron LHC ~1/ page 10

11 Top Quark Decay Top quarks decay predominantly (~100%) to a W-Boson and a b-quark Top-Antitop Signatures determined by the W decay modes: `dilepton channel' ~5% : 2 jets, 2 charged leptons, 2 neutrinos τ+x 44 % µ+jets e+jets e+e e+µ µ+µ hadronic 22 % 14.8 % 1.4 % 1.4 % 14.8 % 2.8 % jet jet `lepton+jets channel' ~30%: 4 jets, 1 charged lepton, 1 neutrino - Large statistics compared to dilepton channel - Clear signature compared to all-jets channel `all-jets channel' ~40%: 6 jets always 2 jets are b-jets I will concentrate on the 'muon+jets channel' b -jet b -jet lepton neutrino page 11

12 Analyses Overview 2 Methods - Common W+jets selection (W decays leptonically to muon or electron) Enhance signal content W+jets tt QCD Topological Likelihood Method: Isolate and estimate signal content, fit ttbar fraction by shape comparison Apply Lifetime b-tagging (Counting Experiment): Further enhance signal content, subtract estimated backgrounds - W+jets - tt W+jets tt QCD QCD page 12

13 Signature - W+Jets Selection Isolated high pt lepton ℓ Hard scatter primary vertex b W+ t p p t q W ℓ - b Neutrino reconstructed as missing transverse energy q' 3 jets with pt > 15 GeV and < 2.5 page 13

14 Jet Reconstruction and Identification The jet reconstruction and identification efficiency in data is not reproduced by the MC: DØ identifies jets in the calorimeter only (cone-algorithm) Determine efficiency in data and in MC and determine MC correction factor Use pt-balanced physics processes q - q, Z, J/, jet g tracks } jet and (Z, J/, ): back-to-back in balanced in p T not balanced in due to z-boost calorimeter cluster Efficiency determination: Select the away-object (,Z, J/, ) Select jet reconstructed only from tracks back-to-back to (Z, J/, ) check how often an associated calorimeter jet is found page 14

15 Muon Isolation Muons are reconstructed in the muon system and in the tracking system Muons coming from a leptonic decay of a W boson tend to be isolated from jets have a relatively high transverse momentum Loose muon isolation: R, je t 0.5 R = 2 2 Muon = MIP (Minimum Ionizing Particle) Tight muon isolation: tracks ET R 0.5 P T calorim eter cells ET 0.06 calorim eter cells R 0.4 E T P R 0.1 T 0.08 page 15

16 1. Topological Analysis Status Moriond 04 page 16

17 Backgrounds Physics Background: Electroweak W production W ℓ+ ℓ additional 4 jets from ISR W+1jet W+2jets... Instrumental Background (fake lepton, fake MET): QCD multijet production Electron Fakes: Electrons faked by (electromagnetic) jets MET Fakes: Misreconstructed calorimeter energy Muon Fakes: Muon-fakes are real muons which are fakely isolated (muons from semileptonic b-decays, where the b-jet is not reconstructed) page 17

18 Determination of QCD Background Determine instrumental QCD multijet background purely from data loose lepton: - Nloose = NQCD + W+jets tt NW+ttbar QCD QCD= 8% W+ttbar= 82% Ntight = QCD* NQCD + W+ttbar* NW+ttbar tight lepton: - W+jets tt QCD Solve linear system of equations for NQCD and NW+ttbar page 18

19 Topological Analysis Overview W+jets QCD - tt loose W selection + 4 jets tight W selection + 4 jets QCD - tt W+jets QCD Determine QCD Background - W+jets tt Combine topological event information in a likelihood discriminant, and perform a fit to the data - tt page 19

20 Event Topology µ ttbar signal b t W- q p t q' µ W+ p q Electroweak W production q q W+ q' µ µ q' b Jets dominated by QCD-Bremsstrahlung: low-energetic in the forward region Jets: high-energetic isotropic Define variables which describe the event topology Criteria: - Good separation power - Low sensitivity to the jet energy scale (dominant systematic uncertainty) page 20 q

21 Topological Variables Variables describing the angular distributions of the physics objects in the final state: Sphericity Aplanarity Sphericity HT2' KTmin' Aplanarity Ratios of energy dependent variables: HT2' (describes centrality) KTmin' (= Rmin(jet,jet) ETmin/ETW) page 21

22 Likelihood Discriminant (LD) i S i L D= i S i i B i Si = ttbar signal Bi = W+jets background i runs over the 4 topological variables Describe data by a linear combination of ttbar, W+4jet and multijet (QCD) Constrain QCD by using the loose and tight system of equations Fit the relative fractions page 22

23 Likelihood Fits for µ+jets & e+jets Fit linear combination of QCD (inverted tight selection in data), W+4jet and ttbar to data µ+jets L=144pb-1 e+jets L=141pb-1 Likelihood Discriminant Likelihood Discriminant page 23

24 Kinematic Distributions e+jets µ+jets page 24

25 Result Comparison with Run I & Summer 03 N t t = BR L sel BR =0.44 L=143 pb 1 sel =10 % Run I summer 03 Dominant uncertainties: Jet energy scale Jet reconstruction efficiency jets e =8.8 stat p p t t X syst ±0.6 lum i pb jets p p t t X = stat 1.6 syst ±0.4 lum i pb pl p jets =7.2 stat t t X syst ±0.5 lum i pb page 25

26 Summary of Topological Analysis & Outlook su m m er 0 3 su m m er 0 3 Summer 03 NOW: Large improvement in statistical and systematic uncertainties Outlook: Publication this fall L = 230 pb-1 Improved systematics page 26

27 2. Lifetime b-tagging Analysis Status ICHEP 04 page 27

28 B-Tagging at DØ Counting Signed Impact Parameter (CSIP) Primary Vertex Secondary Vertex Tagger (SVT) Secondary Vertex DCA (DCA = Distance to Closest Approach) Explicitly reconstruct vertices which are significantly displaced from the primary vertex V0 Filter: Remove track pairs in the mass windows corresponding to KS, and photon conversions ( -> e+e-) I will concentrate on the SVT results Count the number of tracks with large positive DCA significance Jet is tagged if Ntracks( >2) > 3 or Ntracks( >3) > 2 page 28

29 Backgrounds Main background: W+jets Use W+jets sample generated with ALPGEN and interfaced to PYTHIA Rely on ratios of the cross sections of the generated sub-processes: W+light W+c W+cc W+b W+bb Apply a matching procedure of partons from the matrix element generator and reconstructed jets eliminate double counting of the exclusive processes reduce sensitivity to parton generation cuts Estimate QCD background purely from data: using the loose and tight system of equations (Can be done before or after tagging) tight loose Subtract small backgrounds using known cross sections: Diboson production: WW l+jets, WZ l+jets, WZ jjll, ZZ lljj Single top production in s- and t-channel Z l+jets page 29

30 Philosophy of this B-Tagging Analysis Present versions of DØ MC do not reproduce the b-tagging efficiency and mistag rate observed in data Tagging algorithm not applied in MC Instead: Measure jet tagging efficiencies in data (for jet flavor = b,c,light) Parameterize as a function of jet E and : jet(flavor)(et, ) T Apply tagging parameterisations (ET, ) to the Monte Carlo jet(flavor) Derive event tagging probabilities: Ptagevent P tag event n 1ta g =1 jets 1 jet fla vo r E T, Determine number of expected tagged events in the MC: Ntag N tag =N untagged P tag event In particular for the W+njets background: P W njets = fla vo r F fla vo r P W njets fla vo r tag tag F fla vo r tag P W b b j j =42 % P tag W j j j j =1% page 30

31 Visualize B-Tag Philosophy Estimate ttbar production cross section from observed excess in the actual number of tagged events with 3 and 4 jets with respect to the background expectation Optimum use of the statistical information: tag tag single tags and double tags P t t =45 % P double =14 % t t page 31

32 B-Tagging Efficiency in Data Challenge: Select pure sample of b-jets in data Enrich dijet data sample in bbbar QCD heavy flavor production: Select jets which contain muons (muon-in-jet) 2 Methods: Use Shape of P to discriminate Trel between heavy and light flavor PTrel = transverse momentum of the muon relative to the (µ+jet) axis jet PTrel µ µ+jet Apply Lifetime Tagger and Soft Lepton Tagger to two samples muon-in-jet muon-in-jet with away jet lifetime tagged (further enriched in heavy flavor) Clearly solvable system of 8 equations with 8 unknowns (purely from data) B-Tagging Efficiency page 32

33 Mistag Efficiency +light flavor Mistag Efficiency = Tagging efficiency for light flavor jets Mis-Tagging not due to lifetime but due to decay length resolution Idea: Measure negative tagging efficiency in a multijet data sample Correct for heavy flavor jets SF heavy flavor (which have a higher negative tagging rate than light flavor jets) Correct for the remaining long lived particles which are not present in negative tagged jets SFlong lived positive tag negative tag Jet Tracks Secondary Vertex Primary Vertex Secondary Vertex +ligh t fla vor =SF heavy flavor SF long lived - page 33

34 Muon+Jets Candidate Event µsv mip signal in calorimeterr calorimeter Jet 3 MTC IP Jet 2 Jet 5 Jet Jet 1 1 IP IP Jet 4 SV SV page 34

35 Result for SVT Tagger Single Tags: Control bins Double Tags: 1.9 = stat syst ±0.5 lum i pb Luminosity = 164 pb-1 Dominant systematic uncertainties: Jet energy scale Jet reconstruction efficiency b-tagging efficiency in data page 35

36 Summary & Outlook Topological Analysis: World best systematics for a topological analysis Forthcoming publication with 230 pb-1 B-Tagging Analysis: Just got ready for ICHEP Forthcoming publication with improved systematics Understanding of data set: jets, leptons, met, b-tagging, triggering,... Development of powerful high mass analysis techniques Prepared for LHC Direct input to various analyses... page 36

37 R=B(t Wb)/B(t Wq) Measurement In the SM, unitary of the CKM matrix: CSIP V tb 2 R= =V tb V tb V ts V td 2 SVT R deduced from the number of single tagged and the number of double tagged events tt versus R (R fixed in the fit) 68% and 90% CL contours CSIP SVT SVT: 0.11 R = stat syst CSIP: 0.17 R = stat syst Good agreement with SM: R=1 page 37

38 Top Mass Measurement Measurements in l+jets channel (~150 pb-1) - template method uses templates for signal and background mass spectra - ideogram method uses analytical likelihood for event to be signal or background for each selected event template method ideogram method systematics limited: - jet energy scale - ttbar modeling - W+jets modeling -... page 38

39 Helicity of the W in ttbar Events V A i g t 1 5 V tb b W 2 2 Top Standard Model weak decay V A coupling as it is for all the other fermions b W b spin =1/2 t spin =1/2 W W+ spin=1 sum t t W W Left Handed fraction F t b W0 Longitudinal fraction F0 Suppressed by the V-A coupling b W+ Right Handed fraction F+ W0 W- Helicity of W manifests itself in decay product kinematics l+jets, 160 pb 1 b tag or topol. selection kinematic ttbar fit boost into W rest frame decay angle distribution F+ < 90% CL no deviations from SM predictions eventually simultaneous fit for F0 and F+ page 39

40 Backup Slides page 40

41 Taggability Probability for a jet to be tagged conveniently broken down in two components: Probability for a jet to be taggable (= taggability ) Probability for a taggable jet to be tagged (= tagging efficiency ) Motivation: Decouple tagging efficiency from issues related to tracking efficiency calorimeter noise which are absorbed in the taggability Taggability: Probability to match ( R<0.5) a calorimeter jets to a jet reconstructed solely from tracks (trackjet) Flavor dependence of taggability: (measured in W+jets MC) Heavy flavor: harder fragmentation, harder track pt spectrum, larger average track multiplicity per jet page 41

42 B-Tagging Efficiency in Data Challenge: Select pure sample of b-jets in data Enrich dijet data sample in bbbar QCD heavy flavor production: Select jets which contain muons (muon-in-jet): b (->c) -> µ (b-jets) c -> µ (c-jets) /K -> µ (light flavor jets) PTrel = transverse momentum of the muon relative to the (µ+jet) axis jet PTrel µ+jet PTrel discriminates between b-jets and non-b-jets Templates: Shape of P distribution for c- and b-jets from MC Trel Shape of P distribution for light flavor jets from data Trel (random tracks in a jet) page 42 µ

43 B-Tagging Efficiency in Data 3 Methods 1.) ST vs. NT (Single Tag vs. No Tag): PTrel Fit Derive b-fraction in data from PTrel fit to the templates data before and after lifetime tagging the muon jet: tag jet tag b N F b = N jet F b 2.) DT vs. ST (Double Tag vs. Single Tag): P Trel Fit Same as 1.) but require that away-jet is lifetime tagged to further enrich the sample in b-jets: data b = tag N D tag F Db 3.) System8: Solely from data tag N away jet 2 samples with different b-fractions muon-in-jet muon-in-jet with away jet lifetime tagged 2 tagging algorithms soft muon tagger SVT tagger Solvable system of 8 equations: before and after tagging with any or both of the taggers data use Method 3. fo r data b data MC in c l = in c l b MC b page 43 tag F b

44 Inclusive b-tagging Efficiency data data M C b in c l = in c l C Mb ttbar MC: page 44

45 QCD Background Determination Method 1: Determine N in untagged QCD sample (topological approach) Event tagging Probability PQCD from data Method 2: tag Determine N directly in QCD tagged sample (topological approach) NQCDtag = PQCDNQCD Caveat: PQCD has to be determined on sample with same heavy flavor fraction same kinematics as the signal sample Caveat: Large statistical uncertainty One has to verify that QCDtag = QCD large statistical uncertainty page 45

46 Flavor Composition of W+Jets Use W+jets samples generated by ALPGEN interfaced to PYTHIA Don't use absolute values of cross sections, but rely on their ratios Apply matching procedure between Matrix Element partons and reconstructed jets (à la Mangano) reduce double counting and sensitivity to parton generation cuts Remarks: Use matching of Matrix Element partons and parton-jets in the future LO estimate of Wbb / Wjj reproduced by NLO? Qualified yes for scale choices >= 50 GeV and pt cuts of about 15 GeV or greater page 46

47 Kinematic Distributions of Tagged Events page 47

48 Result for CSIP Tagger Single Tags: Control bins Double Tags: 1.9 = stat syst ±0.5 lum i pb Luminosity = 164 pb-1 Dominant systematic uncertainties: Jet energy scale Jet reconstruction efficiency b-tagging efficiency in data page 48

49 DØ Tracking System page 49

50 Particle Identification at DØ Interaction Point Tracking System with magnetic field Inner Silicon Detectors Calorimeter induces showers in dense material EM Layers Had. Layers Muon Detectors Absorber Material Electrons Jets Quarks and Gluons Muons Curvature > Momentum page 50