1. 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

2. 2 Outline 1.Top Quark Production at the Tevatron 2.Motivation for Single Top 3.The Experimental Challenge 4.Analyses Techniques at CDF Likelihood Function Analysis Neural Network Analysis Matrix Element Analysis (NEW) 5.Results with 695/pb and 955/pb 6.Conclusions

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

4. 4 Top Quark Production at Tevatron s-channel  NLO = 0.88±0.07 pb t-channel  NLO = 1.98±0.21 pb  NLO = 6.7 pb Observed 1995! Wanted! 2006/7? B.W. Harris, E. Laenen, L. Phaf, Z. Sullivan, S.Weinzierl hep-ph/0207055 (2002) All cross-sections at M top =175GeV/c 2 We are in the eleventh year since top discovery! No evidence for single top yet! The challenge is the large W+jets background!

5. 5 Motivation

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

7. 7  s (pb)  t (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 W KK (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. 8 Experimental Challenge

9. 9   = 1.0  = 2.8  = 2.0 CDF II Detector ■ Silicon tracking detectors ■ Central drift chambers (COT) ■ Solenoid Coil ■ EM calorimeter ■ Hadronic calorimeter ■ Muon scintillator counters ■ Muon drift chambers ■ Steel shielding

10. 10 Single-Top Signature at CDF 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) 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. 11 Bottom Quark Tagging Secondary Vertex Tagging displaced secondary vertexSignature of bottom quark decay is a displaced secondary vertex L xy ~ 3mmUse long lifetime of B hadrons: c  ~450  m + large boost from top decay  B hadrons travel L xy ~ 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 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

12. 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! 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 W(l ) + 2 jets + b-tag + discriminant 2167~1/3~ 2.6!

13. 13 Summary of Backgrounds 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 Mistags (W+2jets) Falsely tagged light quark or gluon jets Mistag probability parameterization obtained from generic jet data 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

14. 14 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

15. 15 Analyses Techniques

16. 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: L xy, 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. 17 Multivariate Analysis Techniques 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

18. 18  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) j i : histogram bin Four background classes used: Wbb, tt, Wcc/Wc and mistags The Likelihood Function Analysis t-channel LF 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 Variables: M l b log(H T * M l b ) E T (jet1) log( ME tchan ) H T Neural Network b-tagger p ik =Normalized bin-content

19. 19 Input Variables to Likelihood Function Analysis

20. 20 Input Variables to Likelihood Function Analysis II

21. 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-channels-channel

22. 22 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 Standard Model

23. 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, 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

24. 24 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

25. 25 Neural Network Analysis - Results Channels+t-channelt-channels-channel Expected 95% C.L. Limit5.7 pb4.2 pb3.7 pb Observed 95% C.L. Limit3.4 pb3.1 pb3.2 pb Best fit combined search: Best fit separate search: Note: Expected limit assumes single top at Standard Model rate

26. 26 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: Single Top kinematic quantities:  2(initial) + 12(final) = 14 degrees of freedom  Assume leptons and angles well measured  3(l)+4(angle)+3(P in =P fin )+1(E in =E fin ) = 11 constraints  14 – 11 = 3 integrals => Integrate over neutrino p z and jet energy of both jets. Input only lepton and 2 jets 4-vectors!

27. 27 Transfer Function E parton E jet E parton E jet Full simulation vs parton energy: Double Gaussian parameterization: where:  E = (E parton –E jet ) Double Gaussian parameterization:

28. 28 Event Probability Discriminant (EPD) Note: Neural Network b-tagger is used as b-jet probability: b 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):

29. 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 b-tagged dilepton+2jets data (99% ttbar) b-tagged lepton+4jets data (85% ttbar) Dilepton+2jets Lepton+4jets

30. 30 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)

31. 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. 32 Hypothesis Test Most sensitive bins 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 Median p-value = 0.6% (2.5  ) Observed p-value = 1.0% (2.3  ) b s+b

33. 33 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

34. 34 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

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

36. 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. 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! 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%

38. 38 Backup Slides Backup

39. 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. 40 Simple 2D ‘Combination’ Divide the 2D (ME-LF) discriminant space into 4 Regions Region 1) "background-like" contains events with EPD<0.9, LF<0.9 Region 2) "background/LFsignal-like" contains events with EPD 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  2 = 4.55/4 P=33.7  2 = 3.37/4 P=49.8 LF Signal Hypothesis Preferred

41. 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. 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. 43 Information used by the Neural Network B-tagger Use NN-btagger output as b-jet probability b: b = 0.5 * ( NNout +1 )

44. 44 Likelihood Function 2D Templates

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

46. 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 E par Transfer Functions act like ‘single top specific corrections’  E = (E part – E jet ) distributions in slices of E part 10 < E part < 60 GeV 80 < E part < 100 GeV 120 < E part < 150 GeV 60 < E part < 80 GeV 150 < E part < 180 GeV 100 < E part < 120 GeV

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

48. 48 Sources of Systematic Uncertainty Systematic (-1  /+1  ) s-channelt-channelAll Single topShape Variations Jet Energy Scale-1.4% / 1.3%-2.4% / 1.8%-2.0% / 1.6%  Initial State Radiation1.1% / -2.0%2.6% / 2.0%2.0% / 0.3%  Final State Radiation1.3% / 1.4%3.4% / 2.2%2.6% / 1.9%  Parton Dist. Function1.0% / -0.6%1.7% / -0.3%1.4% / -0.4%  Monte Carlo Generator1%2%1.6% Event Det. Efficiency6.1%7.8%7.4% Luminosity6% Neural Net b-taggerN/A  Mistag ModelN/A  Non-W ModelN/A  Q 2 Scale in Alpgen MCN/A  Total Rate Uncertainty9.1%11.3%10.5%N/A CDF RunII Preliminary, L=955pb -1 All rate and shape systematic uncertainties are included as nuisance parameters in the analyses!

49. 49 Kinematic Fitter used in Neural Net and Likelihood Analysis In the top mass reconstruction we have ambiguities from: choosing the P z (ν) solution from W-mass constraint choosing b quark from top decay (s-channel) Use a  2 in which P b, MET, Φ v 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 p z solutions

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

51. 51 Lepton matching

52. 52 Jet1 matching Jet1 P T matching Jet1 Phi matching Jet1 Eta matching Jet1 dR to parton

53. 53 Jet2 matching Jet2 P T matching Jet2 Phi matching Jet2 dR to parton Jet2 Eta matching