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Search for Single Top at CDF

Search for Single Top at CDF. Bernd Stelzer, UCLA on behalf of the CDF Collaboration Fermilab, December 1st 2006. Outline. Single Quark Production at the Tevatron Motivation for Single Top Search The Experimental Challenge Analysis Techniques at CDF

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Search for Single Top at CDF

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  1. Search for Single Top at CDF Bernd Stelzer, UCLA on behalf of the CDF Collaboration Fermilab, December 1st 2006

  2. Outline • Single Quark Production at the Tevatron • Motivation for Single Top Search • The Experimental Challenge • Analysis Techniques at CDF • Likelihood Function Analysis (955 pb-1) • Neural Network Analysis (700 pb-1) • Matrix Element Analysis (955 pb-1) • New Results • Conclusions

  3. The Tevatron Collider • Tevatron produces per day: ~ 40 top pair events ~ 20 single top events Cross Sections at s = 1.96 TeV

  4. Top Quark Production NLO = 6.7±0.8 pb Current World average: Mtop = 171.4  2.1 GeV/c2 Observed 1995! Vtb • Directly measure Vtb • Single Top ~ (Vtb)2 • Source of ~100% • polarized top quarks Wanted! 2006/7? s-channel NLO = 0.88±0.07 pb t-channel NLO = 1.98±0.21 pb Quoted cross-sections at Mtop=175GeV/c2 B.W. Harris et. al, hep-ph/0207055, Z. Sullivan hep-ph/0408049

  5. 1.25 t (pb) s (pb) Sensitivity to New Physics • Single top rate can be altered due to the presence of new Physics • Heavy W boson, charged Higgs H+, Kaluza Klein excited WKK (s-channel signature) • Flavor changing neutral currents: t-Z/γ/g-c couplings (t-channel signature) W,H+ Z c t s-channel and t-channel have different sensitivity to new physics Tait, Yuan PRD63, 014018(2001)

  6. Experimental Challenge

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

  8. Data Collected at CDF Delivered : 2.1 fb-1 Collected : 1.7 fb-1 Tevatron people are doing a fantastic job! 2fb-1 party coming up! This analysis: 955/pb (All detector components ON) Design goal CDF is getting faster, too! 6 weeks turnaround time to calibrate, validate and process raw data

  9. Electron Jet2 Jet1 Single Top Selection CDF W+2jet Candidate Event: Close-up View of Layer 00 Silicon Detector Event Selection: • 1 Lepton, ET >15 GeV, ||< 2.0 • Missing ET (MET)> 25 GeV • 2 Jets, ET > 15 GeV, ||< 2.8 • Veto Fake W, Z, Dileptons, Conversions, Cosmics • At least one b-tagged jet, (secondary vertex tag) 12mm Run: 205964, Event: 337705 Electron ET= 39.6 GeV, MET = 37.1 GeV Jet 1: ET = 62.8 GeV, Lxy = 2.9mm Jet 2: ET = 42.7 GeV, Lxy = 3.9mm

  10. 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 and jets with semileptonic b-decay • 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

  11. Single top hidden behind background uncertainty!  Makes counting experiment impossible! Signal and Background Event Yield CDF Run II Preliminary, L=955 pb-1Event yield in W+2jets

  12. Jet Flavor Separation • Distinguish b-quark jets from charm / light jets using a Neural Network trained with secondary vertex information • Applied to b-tagged jets with secondary vertex • 25 input variables: Lxy, vertex mass, track multiplicity, impact parameter, semilepton decay information, etc... • Good jet-flavor separation! • Independent of b-jet source • Used in all three single top analyses

  13. Jet Flavor Separation II • Fit to W+jets data shows good shape agreement • Fit result consistent with background estimate W + 2 jet events with ≥1 b-tag

  14. Analysis Techniques

  15. Result Analysis Flow Chart CDF Data 3 Analysis Techniques Analysis Event Selection Apply MC Corrections Monte Carlo Signal/Background Signal Background Template Fit to Data Cross Section Discriminant

  16. Analysis Techniques Likelihood Analysis Neural Network Analysis Matrix Element Analysis

  17. The Likelihood Function Analysis Nsig Nbkg i, indexes input variable • t-channel LF Input Variables: • total transverse energy: HT • Mlb (neutrino pz from kin. fitter) • Cos(lepton,light jet) in top decay frame • Qlepton*untagged jet aka QxEta • mj1j2 • log(MEtchan) from MADGRAPH • Neural Network b-tagger • LF=0.01 for double tagged events • s-channel LF Input Variables: • Mlb • log(HT* Mlb) • ET(jet1) • log(MEtchan) • HT • Neural Network b-tagger

  18. Likelihood Function Analysis Background Signal Background Signal Background Signal tchan schan Wbb ttbar Wbb ttbar tchan schan tchan schan Unit area Wbb ttbar

  19. Likelihood Function Discriminants Signal Background Signal Background tchan schan Wbb ttbar Wbb ttbar tchan schan Unit Area Templates normalized to prediction Templates normalized to prediction s-channel t-channel

  20. Analysis Techniques Likelihood Analysis Neural Network Analysis Matrix Element Analysis

  21. Neural Network Analysis - Combined Search • Single Neural Network trained with SM combination of s- and t-channel as signal • 14 Variables: top and dijet invariant masses, Qlxq, angles, jet ET1/2 and j1+ j2, W-boson , lepton pT, kinematic top mass fitter quantities, Neural Network b-tag output etc.. Current result using 695/pb (update with 955/pb expected shortly!) Yield Estimate [695/pb]: Single-Top: 28±3 events, Total Background: 646±96 events

  22. Neural Network Analysis - Separate Search • Two NN’s trained separately for s-channel and t-channel (similar variables) t-channel W+heavy flavor s-channel ttbar

  23. Analysis Techniques Likelihood Analysis Neural Network Analysis Matrix Element Analysis

  24. Matrix Element Approach • Inspired by D0/CDF Matrix Element top mass analyses • Here, we apply the method to a search! • Attempt to include all available kinematic information: • Calculate an event-by-event probability (based on fully differential cross-section calculation) for signal and background hypothesis

  25. 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)

  26. 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

  27. Event Probabilty Discriminant S/B~1/15, S/B~1.6 All events Templates normalized to prediction S/B~1/3 S/B~2.5 In most sensitive bins! (EPD>0.8)

  28. Cross-Checks

  29. Cross-Checks in Data Control Samples • Validate method using data without looking at single top candidates • Compare the Monte Carlo prediction of the discriminant shape to • various control samples in data • W+2 jets data (veto b-jets, orthogonal to our candidate sample)

  30. 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 CDF Run II Preliminary

  31. Template Fit to the data

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

  33. Likelihood Fit to Data • The distribution of the discriminant in data is a superposition • of the single top and several background template distributions • Obtain most probable single top content in data by performing a binned maximum likelihood fit • Background templates are allowed to float in the fit within their rate uncertainties (Gaussian constrained) • Other sources of systematic uncertainty (rate and shape) are included as nuisance parameters in the likelihood function and are also allowed to float within their uncertainties

  34. 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 systematics Shape systematics Discriminant

  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. Sources of Systematic Uncertainty CDF RunII Preliminary, L=955pb-1

  37. Discovery Potential

  38. Signal Sensitivity • We use the CLs Method developed at LEP L. Read, J. Phys. G 28, 2693 (2002) T. Junk, Nucl. Instrum. Meth. A 434, 435 (1999) • http://www.hep.uiuc.edu/home/trj/cdfstats/mclimit_csm1/ • Compare two models at a time • Define Likelihood ratio test statistic: • Systematic uncertainties included in pseudo-experiments • Use median p-value as expected sensitivity Less signal like More signal like Median

  39. Results

  40. Neural Network Results Best fit Separate Search: Best fit Combined Search: • Analysis very correlated with Likelihood • Function analysis • Expected sensitivity similar to Matrix Element

  41. Likelihood Function Results Best fit Separate Search: Best fit Combined Search: 95% upper limit on combined single top cross section Current result excludes models beyond the Standard Model Note: Expected limit assumes no single top

  42. Matrix Element Technique - Result • Matrix Element analysis observes excess over background expectation • Likelihood fit result for combined search:

  43. Observed p-value CDF RunII Preliminary, L=955pb-1 CDF RunII Preliminary, L=955pb-1 b s+b -2lnQ

  44. 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

  45. QxEta for Candidate Events in Signal Region Look for signal features (QxEta) in signal region 4) EPD>0.95 3) EPD>0.90 2) EPD>0.80 1) EPD>0.60

  46. QxEta Distributions in Signal Region 1) 2) 3) 4)

  47. Compatibility of the New Results • Performed common pseudo-experiments • Use identical events • ME uses only 4-vectors of lepton, Jet1/Jet2 • LF uses sensitive event variables • Correlation among fit results: ~53% • 6% of the pseudo-experiments had a difference in fit results at least as large as the difference observed in data CDF II data The result we observe in the data is compatible at the ~6% level

  48. Candidate Events in 2D LF and ME Discriminant Space • Divide the 2D discriminant space of the Matrix Element and Likelihood Function analysis into 4 regions • Define combined background region (1) and combined signal region (1) • Look also at mixed regions (2,3) LF Signal Hypothesis Preferred 2 prob 33.7% 2 prob 49.8% 1 2 3 4 1 2 3 4 Null hypothesis Signal hypothesis

  49. Conclusions • Single top production probes Vtb and is sensitive new physics • We improved sensitivity by a factor of 3-4 compared to published results • We now have 2 - 2.5  sensitivity to single top per analysis! • Presented three analyses using different techniques to separate signal from huge background • Results consistent at 6% level but it's interesting that they show differences • With more data and further improvements we learn what the data is telling us • Exciting times! Back to work!

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