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Evidence for Single Top Quark Production at CDF

Evidence for Single Top Quark Production at CDF. Bernd Stelzer University of California, Los Angeles. HEP Seminar, University of Pennsylvania September, 18th 2007. Outline. Introduction to Top Quarks Motivation for Single Top Search The Experimental Challenge Analysis Techniques

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Evidence for Single Top Quark Production at CDF

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  1. Evidence for Single Top Quark Production at CDF Bernd Stelzer University of California, Los Angeles HEP Seminar, University of Pennsylvania September, 18th 2007

  2. Outline • Introduction to Top Quarks • Motivation for Single Top Search • The Experimental Challenge • Analysis Techniques • Likelihood Function Discriminant (1.51fb-1) • Matrix Element Analysis (1.51fb-1) • Measurement of |Vtb| • More Tevatron Results • Summary / Conclusions / Outlook

  3. The Tevatron Collider • Tevatron is worlds highest energy Collider (until 2008) • Proton Anti-proton Collisions at ECM=1.96 TeV

  4. Top Production at the Tevatron Once every 10,000,000,000 inelastic collision..

  5. Top Production at the Tevatron • At the Tevatron, top quarks are primarily produced in pairs via the strong interaction: • Single top quark production is also predicted by the Standard Model through the electroweak interaction: (st ~ ½tt) Discovered 1995! NLO = 6.7±0.8 pb mt=175GeV/c2 s-channel NLO = 0.88±0.07 pb t-channel NLO = 1.98±0.21 pb Cross-sections at mt=175GeV/c2, B.W. Harris et al., Phys.Rev. D70 (2004) 114012, Z. Sullivan hep-ph/0408049

  6. >10 orders of magnitude! Top Quark in the Standard Model • Top Quark is heaviest particle to date • mt=170.9  1.8 GeV/c2 March 2007 • Close to the scale of electroweak symmetry breaking • Special role in the Standard Model? • Top Quark decays within ~10-24s • No time to hadronize • We can study a ‘bare quark’

  7. Source of single ~100% polarized top quarks: Short lifetime, information passed to decay products Test V-A structure of W-t-b vertex Allows direct Measurement of CKM- Matrix Element Vtb: single top ~|Vtb|2 indirect determinations of Vtb enforce 3x3 unitarity Direct measurements Ratio from Bs oscillations Not precisely measured Why measure Single Top Production ? Ceccucci, Ligeti, Sakai PDG Review 2006 Precision EW rules out “simple” fourth generation extensions, but see J. Alwall et. al., “Is |Vtb|~1?” Eur. Phys. J. C49 791-801 (2007). Vtb s-channel t-channel

  8. 1.25 t (pb) s (pb) Sensitivity to New Physics and Benchmark for WH • Single top rate can be altered due to the presence of New Physics: • t-channel signature: Flavor changing neutral currents (t-Z/γ/g-c couplings) • - s-channel signature:Heavy W boson, charged Higgs H+, Kaluza Klein excited WKK Z c t W,H+ • s-channel single top has the same final state • as WHlbb • => benchmark for WH! Tait, Yuan PRD63, 014018(2001) CMSSM Study: Buchmuller, Cavanaugh, deRoeck, S.H., Isidori, Paradisi, Ronga, Weber, G. Weiglein’07] (WH ~ 1/10s-channe))

  9. Experimental Challenge

  10. Top Pair Production with decay Into Lepton + 4 Jets final state are very striking signatures! Jet3 Electron Jet1 Single top Production with decay Into Lepton + 2 Jets final state Is less distinct! Jet2 Jet4 Event Signatures MET

  11. CDF II Detector (Cartoon) • Silicon tracking detectors • Central drift chambers (COT) • Solenoid Coil • EM calorimeter • Hadronic calorimeter • Muon scintillator counters • Muon drift chambers • Steel shielding h = 1.0 h = 2.0  h = 2.8 Single top analysis needs full detector! Thanks to great work of detector experts and shift crew!

  12. CDF II Detector Central calorimeters Central muon Endplug calorimeters Drift chamber tracker Silicon detector

  13. Data Collected at CDF This analysis uses 1.51 fb-1 (All detector components ON) Delivered : 3.0 fb-1 Collected : 2.7 fb-1 Tevatron people are doing a fantastic job! 3fb-1 party coming up! Design goal CDF is getting faster, too! 6 weeks turnaround time to calibrate, validate and process raw data

  14. Electron Jet2 Jet1 Single Top Selection Event Selection: • 1 Lepton, ET >20 GeV, |e()|< 2.0 (1.0) • Missing ET, (MET) > 25 GeV • 2 Jets, ET > 20 GeV, ||< 2.8 • Veto Fake W, Z, Dileptons, Conversions, Cosmics • At least oneb-tagged jet, (displaced secondary vertex tag) CDF W+2jet Candidate Event: Close-up View of Layer 00 Silicon Detector 12mm Run: 205964, Event: 337705 Electron ET= 39.6 GeV, Missing ET = 37.1 GeV Jet 1: ET = 62.8 GeV, Lxy = 2.9mm Jet 2: ET = 42.7 GeV, Lxy = 3.9mm

  15. B-quark Tagging and Jet Flavor Separation • Separate tagged b-jets from charm/light jets using a Neural Network trained with tracking information • Lxy, vertex mass, track multiplicity, impact parameter, semilepton decay information, etc... • Used in all single top analyses • Exploit long lifetime of B hadrons (c ~450 m)+boost • B hadrons travel Lxy~3mm before decay with large track multiplicity Charm tagging rate ~10% Mistag rate ~ 0.5% Neural Network Jet-Flavor Separator NN Output

  16. Background Estimate • W+HF jets (Wbb/Wcc/Wc) • W+jets normalization from data and heavy flavor (HF) fractions from ALPGEN Monte Carlo • Top/EWK (WW/WZ/Z→ττ, ttbar) • MC normalized to theoretical cross-section • Non-W (QCD) • Multijet events with semileptonic b-decays or mismeasured jets • Fit low MET data and extrapolate into signal region Z/Dib tt non-W Wbb Mistags • W+HF jets (Wbb/Wcc/Wc) • W+jets normalization from data and • heavy flavor (HF) fraction from MC Wcc Wc • Mistags (W+2jets) • Falsely tagged light quark or gluon jets • Mistag probability parameterization obtained from inclusive jet data

  17. Non-W Estimate • Build non-W model from ‘anti-electron’ selection • Require at least two non-kinematic lepton ID variables to fail: • EM Shower Profile 2, shower maximum matching (dX and dZ), Ehad/Eem, • Data is superposition of non-W and W+jets contribution -> Likelihood Fit Before b-tagging: After b-tagging: Signal Region Signal Region

  18. Note: Similar for W+charm background Correct data for non W+jets events Heavy flavor fractions and b-tagging efficiencies from LO ALPGEN Monte Carlo Calibrate ALPGEN heavy flavor Fractions by comparing W + 1jet Data with ALPGEN jet Monte Carlo KHF=1.4 ± 0.4 Large uncertainties from Monte Carlo estimate and heavy flavor calibration (36%) W + Heavy Flavor Estimate • Method inherited from CDF Run I (G. Unal et. al.) • Measure fraction of W+jets events with heavy flavor (b,c) in Monte Carlo • Normalize fractions to W+jets events found in data

  19. Single top swamped by background and hidden behind background uncertainty.  Makes counting experiment impossible! Signal and Background Event Yield CDF RunII Preliminary, L=1.51 fb-1Predicted Event Yield in W+2jets

  20. Analysis Flow Chart CDF Data Analysis Technique Analysis Event Selection Apply MC Corrections Monte Carlo Signal/Background Result Signal Background Template Fit to Data Cross Section Discriminant

  21. Analysis Techniques Likelihood Discriminant Matrix Element Analysis More Tevatron Results

  22. The Likelihood Function Analysis Bkgr tchan schan Signal Wbb ttbar Nsig Unit Area Nbkg Discriminant i, index input variable Leading Jet ET (GeV) Uses 7 (8) kinematic variables for t-channel (s-channel) Likelihood Function e.g. M(Wb) or kin. Solver 2, HT, QxEta, NN flavor separator, Madgraph Matrix Elements, M(jj)

  23. Wbb ttbar Kinematic Variables HT =ET(lepton,MET,Jets) Background Signal Background Signal tchan schan Wbb ttbar tchan schan Wbb ttbar tchan schan

  24. Analysis Techniques Likelihood Discriminant Matrix Element Discriminant More Tevatron Results

  25. Matrix Element Approach • No single ‘golden’ kinematic variable! • Attempt to include all available kinematic information by • using Matrix Element approach • Start from Fermi’s Golden rule: • Cross-sections ~ |Matrix Element|2 Phase space • Calculate an event-by-event probability (based on fully differential cross-section calculation) for signal and background hypothesis

  26. c Matrix Element Method Event probability for signal and background hypothesis: Leading Order matrix element (MadEvent) W(Ejet,Epart) is the probability of measuring a jet energy Ejet when Epart was produced Integration over part of the phase space Φ4 Input only lepton and 2 jets 4-vectors! Parton distribution function (CTEQ5)

  27. Double Gaussian parameterization: Eparton Ejet Transfer Functions Full simulation vs parton energy: Eparton Ejet Double Gaussian parameterization: where:  E = (Eparton–Ejet)

  28. Event Probability Discriminant (EPD) • We compute probabilities for signal and background hypothesis per event • Use full kinematic correlation between signal and background events • Define ratio of probabilities as event probability discriminant (EPD): ;b = Neural Network b-tagger output Background Signal

  29. Event Probabilty Discriminant • S/B~1/17 over full range • Likelihood fit will pin down • background in low score region S/B~1/1 In most sensitive bin!

  30. Cross-Checks

  31. Cross-Checks in Data Control Samples • Validate method in various data control samples • W+2 jets data (veto b-jets, selection orthogonal to the candidate sample) • Similar kinematics, with very little contribution from top (<0.5%) px py pz E Lepton (Electron/Muon) Leading Leading Jet Second Leading Jet

  32. Cross-Checks in Data Control Samples • b-tagged dilepton + 2 jets sample • Purity: 99% ttbar • Discard lepton with lower pT • b-tagged lepton + 4 jets sample • Purity: 85% ttbar • Discard 2jets with lowest pT CDF Run II Preliminary

  33. Monte Carlo Modeling Checks

  34. Template Fit to the data

  35. Binned Likelihood Fit • Binned Likelihood Function: • Expected mean in bin k: • All sources of systematic uncertainty included as nuisance parameters • Correlation between Shape/Normalization uncertainty considered (δi) βj= σj/σSM parameter single top (j=1) W+bottom (j=2) W+charm (j=3) Mistags (j=4) ttbar (j=5) k = Bin index i = Systematic effect δi = Strength of effect εji± = ±1σ norm. shifts κjik± = ±1σshift in bin k

  36. Rate vs Shape Systematic Uncertainty Systematic uncertainties can affect rate and template shape • Rate systematics give fit templates freedom to move vertically only • Shape systematics allow templates to ‘slide horizontally’ (bin by bin) Rate and Shape systematics Discriminant

  37. Sources of Systematic Uncertainty CDF RunII Preliminary, L=1.51fb-1

  38. Results

  39. Matrix Element Analysis • Matrix Element analysis observes excess over background expectation • Likelihood fit result for combined search:

  40. ME Separate Search • Perform separate likelihood fit for • s-channel and t-channel signal • Both signal templates float independently s-channel s=1.1 pb +1.0 −0.8 t-channel t=1.9 pb +1.0 −0.9

  41. Likelihood Function Discriminant • Likelihood Function analysis also observes excess over background expectation • Observed deficit previously in 0.955 fb-1

  42. Likelihood Function 2D Fit

  43. Signal Significance

  44. 3.1 Evidence Hypothesis Testing L. Read, J. Phys. G 28, 2693 (2002) T. Junk, Nucl. Instrum. Meth. A 434, 435 (1999) • Calculate p-value: Faction of background-only pseudo-experiments with a test statistic value as signal like (or more) as the value observed in data • Define Likelihood ratio test statistic: • Systematic uncertainties included in pseudo-experiments • Use median p-value as measure for the expected sensitivity Less signal like More signal like

  45. Hypothesis Testing Less signal like More signal like

  46. Signal Features

  47. Single Top Candidate Event Central Electron Candidate Charge: -1, Eta=-0.72 MET=41.85, MetPhi=-0.83 Jet1: Et=46.7 Eta=-0.61 b-tag=1 Jet2: Et=16.6 Eta=-2.91 b-tag=0 QxEta = 2.91 (t-channel signature) EPD=0.95 Run: 211883, Event: 1911511 Jet1 Lepton Jet2

  48. Single Top Signal Features Look for signal features in high score output EPD>0.95 EPD>0.90

  49. QxEta Distributions in Signal Region EPD>0.9 EPD>0.95 3) 4)

  50. m(W,b) Distributions in Signal Region EPD>0.9 EPD>0.95

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