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New Results from CDF Single Top Searches

New Results from CDF Single Top Searches. Bernd Stelzer UCLA. on behalf of the CDF Collaboration. 2006 Joint Meeting of Pacific Region Particle Physics Communities Honolulu, Hawaii, November 1 st 2006. Outline. Top Quark Production at the Tevatron Motivation for Single Top

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New Results from CDF Single Top Searches

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  1. New Results from CDFSingle Top Searches Bernd Stelzer UCLA on behalf of the CDF Collaboration 2006 Joint Meeting of Pacific Region Particle Physics Communities Honolulu, Hawaii, November 1st 2006

  2. Outline • Top Quark Production at the Tevatron • Motivation for Single Top • The Experimental Challenge • Analyses Techniques at CDF • Likelihood Function Analysis • Neural Network Analysis • Matrix Element Analysis (NEW) • Results with 695/pb and 955/pb • Conclusions

  3. The Tevatron Collider • Tevatron is a proton-antiproton collider with ECM=1.96 TeV • Only place where top quarks are produced • ~1/fb for analysis (good silicon), >= 1.3/fb being processed! Cross Sections at Ös = 1.96 TeV

  4. Top Quark Production at Tevatron NLO = 6.7 pb • We are in the eleventh year since top discovery! • No evidence for single top yet! • The challenge is the large W+jets background! Observed 1995! All cross-sections at Mtop=175GeV/c2 Wanted! 2006/7? s-channel NLO = 0.88±0.07 pb t-channel NLO = 1.98±0.21 pb B.W. Harris, E. Laenen, L. Phaf, Z. Sullivan, S.Weinzierl hep-ph/0207055 (2002)

  5. Motivation

  6. Single Top within the Standard Model • Cross section is proportional to |Vtb|2 • Single top allows direct measurement • No assumption about unitarity of CKM matrix (using unitarity we know: Vtb = 0.99) • Source of ~100% polarized top quarks • Test W-t-b coupling (V-A) • Important background to low mass Higgs (mH<130 GeV) WH • Single top analysis is a benchmark for the WH analysis, WH ~ 1/10 Single Top (S. Willenbrock, Rev. Mod. Phys. 72, 1141-1148) (G. Mahlon, hep-ph/9811219) cos(lepton,d-quark) in top frame

  7. t (pb) s (pb) Single Top Beyond the Standard Model • 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) • 4th generation of quarks • s-channel and t-channel have different sensitivity to new physics • Given speculation that the top quark may play a special role in electroweak symmetry breaking, studying the top quark’s electroweak production is important! Tait, Yuan PRD63, 014018(2001)

  8. Experimental Challenge

  9. CDF II Detector • 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

  10. Single-Top Signature at CDF 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) • Single top mostly in the W+2 jets bin • W+1 jet is dominated by W+jet (S/B~1/72) • W+3 jets is dominated by ttbar (S/B~1/28) • (investigating gain in sensitivity)

  11. Electron Jet2 Jet1 Bottom Quark Tagging • Secondary Vertex Tagging • Signature of bottom quark decay is a displaced secondary vertex • Use long lifetime of B hadrons: c ~450 m + large boost from top decay B hadrons travel Lxy~ 3mm before decaying with large charged track multiplicity • Tagging efficiency per jet ~40% CDF W+2jet Candiate Event: Close-up View of Layer 00 Silicon Detector 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

  12. The Experimental Challenge • Single top search suffers from large amount of W+jets backgrounds • b-tagging is essential for the analysis to improve signal purity • The use of multivariate analysis techniques to distinguish signal from background and a good understanding of background is key!

  13. Summary of Backgrounds • 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 generic jet data

  14. 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

  15. Analyses Techniques

  16. Neural Network Extension to b Tagging • A large fraction of backgrounds are W+charm jets and Mistags! • Distinguish b-quark tags from charm / mistags 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 separation! • Network output is used as continuous input variable in all multivariate single top analyses W + 2 jet events with ≥1 b-tag

  17. Multivariate Likelihood Method Based on collection of signal and background Monte Carlo distributions Multiply probability densities for each variable in the signal templates, divide by sum of probability densities in signal and background Neural Network Train artificial neural network on distributions of signal and background Monte Carlo events Maps correlated input distributions to continuous output distribution between -1 (background) and +1 (signal) Matrix Element Technique For each candidate calculate an event probability d/ for signal and background hypothesis Build discriminant based on event probabilities All analysis techniques construct a final discriminating variable which is evaluated for signal/background Monte Carlo and fitted to the data Multivariate Analysis Techniques

  18. The Likelihood Function Analysis • Multiply probability densities for signal input variables, and divide by sum of probability densities in signal and background • i: variable index, k: sample index (s or t) ji: histogram bin • Four background classes used: Wbb, tt, Wcc/Wc and mistags pik=Normalized bin-content • t-channel LF 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 Variables: • Mlb • log(HT* Mlb) • ET(jet1) • log(MEtchan) • HT • Neural Network b-tagger

  19. Input Variables to Likelihood Function Analysis

  20. Input Variables to Likelihood Function Analysis II

  21. Likelihood Function Discriminants • Unfortunately, there is no single ‘golden’ variable to do the single top search • Combining information from several ‘input variables’ in likelihood function discriminant is powerful • Both, s-channel and the t-channel likelihood function discriminants show deficit in signal region! t-channel s-channel

  22. 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 Standard Model Note: Expected limit assumes no single top

  23. 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

  24. 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

  25. Neural Network Analysis - Results Best fit separate search: Best fit combined search: Note: Expected limit assumes single top at Standard Model rate

  26. Matrix Element Method Single Top kinematic quantities: • 2(initial) + 12(final) = 14 degrees of freedom • Assume leptons and angles well measured • 3(l)+4(angle)+3(Pin=Pfin)+1(Ein=Efin) = 11 constraints • 14 – 11 = 3 integrals => Integrate over neutrino pz and jet energy of both jets. Leading Order matrix element (MadEvent) W(Ejet,Epart) is the probability of measuring a jet energy Ejet when Epart was produced Event probability for signal and background hypothesis: Integration over part of the phase space Φ4 Input only lepton and 2 jets 4-vectors! Parton distribution function (CTEQ5)

  27. Transfer Function Full simulation vs parton energy: Double Gaussian parameterization: Eparton Ejet 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): Note: Neural Network b-tagger is used asb-jet probability: b

  29. Cross-Checks in Data `Control Regions • Validate method using data without • looking at single top candidates • Compare the Monte Carlo prediction of • the shape of the discriminant to various • control samples in data • W+2 jets data (require no b-tagged jet) CDF Run II Preliminary CDF Run II Preliminary • b-tagged dilepton+2jets • data (99% ttbar) • b-tagged lepton+4jets • data (85% ttbar) Dilepton+2jets Lepton+4jets

  30. Input Variables to Matrix Element Analysis • Input to the Matrix Element Analysis are the measured four-vectors • of the Lepton, Jet1 and Jet2 in the W+2jets data (>=1 b-tagged jet) Jet1 Jet2 Lepton

  31. Look at Data • Matrix Element analysis observes excess over background expectation • Likelihood fit result for combined search: • Hypothesis test based on CLs method

  32. Hypothesis Test 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/ Define Likelihood ratio test statistic: CDF RunII Preliminary, L=955pb-1 b s+b Most sensitive bins

  33. 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

  34. 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

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

  36. Compatibility of the New Results • Performed common pseudo-experiments • Fitting EPD and LF discriminants • Correlation among fit results: ~53% • 6% of the pseudo-experiments had a difference in fit results at least as bad as the difference observed in data • The results we observe in the data are compatible at the ~6% level Compatibility of the two new results?

  37. Conclusions • Search for Single Top is an exciting challenge! • We developed three powerful analysis techniques at CDF • New results with 695/pb and 955/pb are promising! • We are on the verge of being sensitive to a combined s+t channel single top signal with an expected sensitivity of 2 - 2.5  per analysis! • Likelihood Function and Matrix Element results consistent at the ~6% level • Plan to combine all three analyses • Looking forward to analyzing more data!

  38. Backup Slides Backup

  39. Independent Compatibility Study • Perform common pseudo-experiments • Compute reference (averaged) fit result for each pseudo-experiment • M = (w1* 1 +w2* 2 )/(w1+w2) • Then compute | 1 -M|/e1 and see how many times this is worse than what we see in data • Result is 6% of the time! • correlation 0.532 • err1 average 0.490 • err2 average 0.527 • Probability1 5.91 • Probability2 6.08 • Similar result by throwing correlated Gaussian random numbers (I.e. fit results)

  40. Simple 2D ‘Combination’ • Divide the 2D (ME-LF) discriminant space into 4 Regions LF Region 1) "background-like" contains events with EPD<0.9, LF<0.9 Region 2) "background/LFsignal-like" contains events with EPD<0.9, LF>0.9 Region 3) "background/MEsignal-like" contains events with EPD>0.9, LF<0.9 Region 4) "signal-like" contains events with EPD>0.9, LF>0.9 Signal Hypothesis Preferred 2 = 3.37/4 P=49.8 2 = 4.55/4 P=33.7

  41. Correlation of Discriminants • s-channel signal: 37.3% • t-channel signal: 65.1% • ttbar: 43.6% • Wbbbar: 53.6% • Wccbar: 59.1% • Wc: 62.3% • total expected: 50% • data: 55.2% Correlation between Likelihood Function and Matrix Element Analysis

  42. Pseudo-Experiments with Features of Results in Data Matrix Element: 1: Likelihood Fkt: 2: =FIT/SM Require: a) 0.9 < 1 < 1.1 b) (1 -2 ) > 2 e1

  43. Information used by the Neural Network B-tagger Use NN-btagger output as b-jet probability b: b = 0.5 * ( NNout +1 )

  44. Likelihood Function 2D Templates

  45. Event probability discriminants s-channel discriminant t-channel discriminant • Overall good separation of signal • from background • For ‘combined search’ define: • Trade less good separation for • higher signal rate combined discriminant

  46. Transfer Function Tests • Jet energies corrected up to Level 5 • Select Jets which are matched to partons (b-quarks here) • Use double Gaussian parameterization (tails) • Compare Transfer Functions for different slices of Epar • Transfer Functions act like ‘single top specific corrections’ 10 < Epart< 60 GeV 60 < Epart< 80 GeV 80 < Epart< 100 GeV 100 < Epart< 120 GeV 120 < Epart< 150 GeV 150 < Epart< 180 GeV • E = (Epart – Ejet) distributions in slices of Epart

  47. Including Systematic Uncertainty (in Likelihood) • Likelihood Function (CDF 7106): • Expected mean in bin k: • Correlation between Shape/Normalization uncertainty included (δi) • Profile Likelihood with respect to all nuisance parameters 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

  48. Sources of Systematic Uncertainty CDF RunII Preliminary, L=955pb-1 • All rate and shape systematic uncertainties are included as • nuisance parameters in the analyses!

  49. Kinematic Fitter used in Neural Net and Likelihood Analysis • In the top mass reconstruction we have ambiguities from: • choosing the Pz(ν) solution from W-mass constraint • choosing b quark from top decay (s-channel) • Use a 2 in which Pb, MET, Φv is allowed to float • central values = measured values • uncertainties derived from HEPG comparisons with reconstructed values • Without looking at the b-tag, minimize 2 under four scenarios • 2 choices of which jet is labeled ‘b from top decay’ • 2 neutrino pz solutions

  50. CLs and p-values CLs+b= P(QQobs|s+b): probability of missing a signal as badly as the data if the signal is really there. CLb = P(QQobs|b): Probability of the background looking more signal like than the observed data. If CLs+b < 0.05, we can reject the s+b hypothesis at the 95% CL. p-value=(1-CLb) < 1.3510-3 “3” excess is present. p-value=(1-CLb) < 2.8710-7 “5” discovery (1-CLb) CLs+b

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