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

2. 2 Outline 1.Single Quark Production at the Tevatron 2.Motivation for Single Top Search 3.The Experimental Challenge 4.Analysis Techniques at CDF Likelihood Function Analysis (955 pb -1 ) Neural Network Analysis (700 pb -1 ) Matrix Element Analysis (955 pb -1 ) 5.New Results 6.Conclusions

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

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

5. 5 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 W KK (s-channel signature) - Flavor changing neutral currents: t-Z/γ/g-c couplings (t-channel signature) Tait, Yuan PRD63, 014018(2001) s-channel and t-channel have different sensitivity to new physics Z c t W,H+W,H+  s (pb) 1.25  t (pb)

6. 6 Experimental Challenge

7. 7 Event Signatures Jet1 Jet2 Electron Jet4 Jet3 MET 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!

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

9. 9 Single Top Selection Event Selection: 1 Lepton, E T >15 GeV, |  |< 2.0 Missing E T (MET) > 25 GeV 2 Jets, E T > 15 GeV, |  |< 2.8 Veto Fake W, Z, Dileptons, Conversions, Cosmics At least one b-tagged jet, (secondary vertex tag) CDF W+2jet Candidate Event: Close-up View of Layer 00 Silicon Detector Run: 205964, Event: 337705 Electron E T = 39.6 GeV, MET = 37.1 GeV Jet 1: E T = 62.8 GeV, L xy = 2.9mm Jet 2: E T = 42.7 GeV, L xy = 3.9mm Jet2 Jet1 Electron 12mm Number of Events / 955 pb -1 Single TopBackground S/B S/  B W(l ) + 2 jets 7415500~1/210~ 0.6 W(l ) + 2 jets + b-tag 38540~1/15~ 1.6

10. 10 Mistags (W+2jets) Falsely tagged light quark or gluon jets Mistag probability parameterization obtained from inclusive jet data Background Estimate W+HF jets (Wbb/Wcc/Wc) W+jets normalization from data and heavy flavor (HF) fraction from MC 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 Wbb Wcc Wc non-W Z/Dib Mistags tt W+HF jets (Wbb/Wcc/Wc) W+jets normalization from data and heavy flavor (HF) fractions from ALPGEN Monte Carlo

11. 11 Signal and Background Event Yield CDF Run II Preliminary, L=955 pb Event yield in W+2jets CDF Run II Preliminary, L=955 pb -1 Event yield in W+2jets Single top hidden behind background uncertainty!  Makes counting experiment impossible! s-channel15.4 ± 2.2 t-channel22.4 ± 3.6 tt58.4 ±13.5 Diboson13.7 ± 1.9 Z + jets11.9 ± 4.4 Wbb170.9 ± 50.7 Wcc63.5 ± 19.9 Wc68.6 ± 19.0 Non-W26.2 ± 15.9 Mistags136.1 ± 19.7 Single top37.8 ± 5.9 Total background549.3 ± 95.2 Total prediction587.1 ± 96.6 Observed644

12. 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: L xy, 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. 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 Background Estimate Neural Network Fit W+bottom 299.0  56.8292.8  26.3 W+charm 148.1  39.4171.6  53.8 Mistags 140.0  19.8179.5  42.5 Sum 587.1  96.6 644.0

14. 14 Analysis Techniques

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

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

17. 17 The Likelihood Function Analysis t-channel LF Input Variables: total transverse energy: H T M l b (neutrino p z from kin. fitter) Cos  (lepton,light jet) in top decay frame Q lepton *  untagged jet aka QxEta m j1j2 log(ME tchan ) from MADGRAPH Neural Network b-tagger LF=0.01 for double tagged events s-channel LF Input Variables: M l b log(H T * M l b ) E T (jet1) log( ME tchan ) H T Neural Network b-tagger N sig N bkg i, indexes input variable

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

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

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

21. 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, Q l x  q, angles, jet E T1/2 and  j1 +  j2, W-boson , lepton p T, 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. 22 Neural Network Analysis - Separate Search Two NN’s trained separately for s-channel and t-channel (similar variables) t-channel W+heavy flavor ttbar s-channel

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

24. 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. 25 Matrix Element Method Parton distribution function (CTEQ5) Leading Order matrix element (MadEvent) W(E jet,E part ) is the probability of measuring a jet energy E jet when E part was produced Integration over part of the phase space Φ 4 Event probability for signal and background hypothesis: Input only lepton and 2 jets 4-vectors! c

26. 26 Event Probability Discriminant (EPD) ;b = Neural Network b-tagger output 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):

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

28. 28 Cross-Checks

29. 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. 30 Cross-Checks in Data Control Samples CDF Run II Preliminary b-tagged dilepton + 2 jets sample Purity: 99% ttbar Discard lepton with lower p T b-tagged lepton + 4 jets sample Purity: 85% ttbar Discard 2jets with lowest p T

31. 31 Template Fit to the data

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

33. 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. 34 Rate vs Shape Systematic Uncertainty Discriminant Rate systematics give fit templates freedom to move vertically only Shape systematics allow templates to ‘slide horizontally’ (bin by bin) Rate systematics Shape systematics Systematic uncertainties can affect rate and template shape

35. 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 (δ i ) β j = σ j /σ SM 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 δ i = Strength of effect ε ji± = ±1σ norm. shifts κ jik± = ±1σ shift in bin k

36. 36 Sources of Systematic Uncertainty Single TopRate VariationsShape Variations Jet Energy Scale  Initial State Radiation  Final State Radiation  Parton Dist. Function  Monte Carlo Generator  Efficiencies / b-tagging SF  Luminosity  Total Rate Uncertainty10.5%N/A CDF RunII Preliminary, L=955pb -1 BackgroundRate Unertainty W+bottom25% W+charm28% Mistag15% ttbar23% BackgroundsRate VariationsShape Variations Jet Energy Scale  Neural Net b-tagger  Mistag Model  Non-W Model  Q 2 Scale in Alpgen MC 

37. 37 Discovery Potential

38. 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 Likelihood Function Analysis: Median p-value = 2.3% (2.0  ) Matrix Element Analysis: Median p-value = 0.6% (2.5  ) Median More signal like Less signal like

39. 39 Results

40. 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. 41 Likelihood Function Results Best fit Separate Search: Best fit Combined Search:  95 s+t channel Expected2.9 pb Observed2.7 pb 95% upper limit on combined single top cross section Note: Expected limit assumes no single top Current result excludes models beyond the Standard Model

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

43. 43 Observed p-value Observed p-value = 1.0% (2.3  ) b s+b CDF RunII Preliminary, L=955pb -1 Observed p-value = 51.3% CDF RunII Preliminary, L=955pb -1 -2lnQ

44. 44 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 Single Top Candidate Event Jet1 Jet2 Lepton Run: 211883, Event: 1911511

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

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

47. 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. 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% 12341234 Null hypothesisSignal hypothesis

49. 49 Conclusions Single top production probes V tb 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! Techniques+t cross-sectionExpected p-valueObserved p-value Likelihood Function (955/pb)0.3(+1.2/-0.3)pb2.3%51.3% Neural Network (695/pb)0.8(+1.3/-0.8)pbcoming soon Matrix Element (955/pb)2.7(+1.5/-1.3)pb0.6%1.0% Combined Analysis (955/pb)coming soon

50. 50 Backup Slides Backup

51. 51 Non-W Estimate Build non-W model from anti-electron selection Require at least two non-kinematic lepton ID variables to fail: E had /E em, EM Shower Profile  2, shower maximum matching (dX and dZ) Data is superposition of non-W and W+jets contribution -> Likelihood Fit Signal Region Before b-tagging:After b-tagging: Signal Region

52. 52 Mistag Estimate Mistag Events arise due to limited detector resolution Parameterize Mistag probability in control data (generic jet sample) Apply Mistag Matrix (MM) to W+jets events in data: Account for material interaction, long lived light particles

53. 53 Correct data for non W+jets events 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 Heavy flavor fractions and b-tagging efficiencies from LO ALPGEN Monte Carlo Calibrate ALPGEN heavy flavor fractions inclusive jet data and Inclusive ALPGEN jet Monte Carlo Note: Similar for W+charm background Large uncertainties from Monte Carlo estimate and heavy flavor calibration (~25-30%) CDF RunII Reference: PhysRevD.71,052003

54. 54 Heavy Flavor Fraction Calibration 1) Estimate generic jet heavy flavor fraction in ALPGEN Monte Carlo 2) Fit for bottom and charm fraction in generic jet data Difference between the two outcomes suggests K=1.5±0.4 Result supported by study using MCFM: J. M. Campbell, J. Houston, Method 2 at NLO, hep-ph/0405276

55. 55 Input Variables to Matrix Element Analysis Lepton Jet1Jet2 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)