Search for Single Top at CDF

Bernd Stelzer, UCLA

on behalf of the CDF Collaboration

Fermilab, December 1st 2006

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

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

s-channelNLO = 0.88±0.07 pb

t-channelNLO = 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 Mtop=175GeV/c2

Vtb•Directly measure Vtb

Single Top ~ (Vtb)2

•Source of ~100% polarized top quarks

NLO = 6.7±0.8 pb

Mtop = 171.4 2.1 GeV/c2Current World average:

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 WKK (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

ct

W,H+

s (pb)

1.25 t (pb)

6

ExperimentalChallenge

7

Event Signatures

Jet1

Jet2

Electron

Jet4

Jet3

MET

Top Pair Production with decayInto Lepton + 4 Jets final stateare very striking signatures!

Single top Production with decayInto Lepton + 2 Jets final stateIs less distinct!

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

Single Top Selection

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)

CDF W+2jet Candidate Event:CDF W+2jet Candidate Event:

Close-up View of Layer 00 Silicon DetectorClose-up View of Layer 00 Silicon Detector

Run: 205964, Event: 337705Electron ET= 39.6 GeV, MET = 37.1 GeVJet 1: ET = 62.8 GeV, Lxy = 2.9mmJet 2: ET = 42.7 GeV, Lxy = 3.9mm

Jet2

Jet1

Electron

12mm

Number of Events / 955 pb-1 Single Top

Background

S/B S/B

W(l) + 2 jets 74 15500 ~1/210

~ 0.6

W(l) + 2 jets + b-tag 38 540 ~1/15 ~ 1.6

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

WccWc

non-W

Z/DibMistags

tt

W+HF jets (Wbb/Wcc/Wc)

•W+jets normalization from data and heavy flavor (HF) fractions from ALPGEN Monte Carlo

11

Signal and Background Event Yield

CDF Run II Preliminary, L=955 CDF Run II Preliminary, L=955 pbpb-1 Event yield in W+2jetsEvent yield in W+2jets

Single top hidden behind background uncertainty! Makes counting experiment impossible!s-channel 15.4 ± 2.2

t-channel 22.4 ± 3.6

tt 58.4 ±13.5

Diboson 13.7 ± 1.9

Z + jets 11.9 ± 4.4

Wbb170.9 ± 50.7

Wcc63.5 ± 19.9

Wc68.6 ± 19.0

Non-W26.2 ± 15.9

Mistags136.1 ± 19.7

Single top 37.8 ± 5.9

Total background

549.3 ± 95.2

Total prediction

587.1 ± 96.6

Observed 644

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

Background

Estimate

Neural Network Fit

W+bottom 299.0 56.8

292.8 26.3

W+charm 148.1 39.4

171.6 53.8

Mistags 140.0 19.8

179.5 42.5

Sum 587.1 96.6

644.0

14

Analysis Techniques

15

Analysis Flow Chart

Analysis Event

Selection

Analysis Event

Selection

CDF Data

CDF Data

Monte CarloSignal/

Background

Monte CarloSignal/

Background

Apply MCCorrection

s

Apply MCCorrection

s

3 Analysis Techniques

3 Analysis Techniques

Result

Template Fit to Data

Template Fit to Data

Discriminant

Signal

Background

Cross Section

16

Analysis Techniques

Likelihood Analysis

Neural Network Analysis

Matrix Element Analysis

17

The Likelihood Function Analysis

t-channel LF Input Variables:•total transverse energy: HT

•Mlb (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:•Mlb

•log(HT* Mlb )•ET(jet1)•log(MEtchan) •HT

•Neural Network b-tagger

pisig

N isig

N isig N i

bkg

Nsig

Nbkg

i, indexes input variable

L(x )

psigi (x i)i1

nvarpsig

i (x i)i1

nvar pbkgi (x i)i1

nvar

18

Likelihood Function Analysis

Background Background SignalSignal Background Signal

Unit area

Wbbttbar

Wbbttbar

Wbbttbar

tchanschan

tchanschan

tchanschan

19

Likelihood Function Discriminants

t-channel s-channel

Background SignalBackground Signal

Unit Area

Wbbttbar

tchanschan

tchanschan

Wbbttbar

Templates normalized to prediction

Templates normalized to prediction

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, Qlxq, 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

ttbar

s-channel

23

Analysis Techniques

Likelihood Analysis

Neural Network Analysis

Matrix Element Analysis

24

Matrix Element Approach

P(x) d (pi

)

1

M

2d

• 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

Matrix Element Method

P( pl, p j1

, p j 2 )

1

d j1d j 2dp

z | M(pi ) |2

f (q1) f (q2)

| q1 ||q2 |4 W jet (E jet,E part)

comb

Parton

distribution function (CTEQ5)

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

Event probability for signal and background hypothesis:

Input only lepton and 2 jets 4-vectors!

c

26

Event Probability Discriminant (EPD)

EPD bPsin gletop

bPsin gletop bPWbb (1 b)PWcc (1 b)PWcj

;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

Event Probabilty Discriminant

S/B~1/3S/B~2.5

In most sensitive bins!(EPD>0.8)

S/B~1/15, S/B~1.6All events

Templates normalized to prediction

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

CDF Run II Preliminary

CDF Run II Preliminary

•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

31

Template Fitto the data

32

Analysis Flow Chart

Analysis Event

Selection

Analysis Event

Selection

CDF Data

CDF Data

Monte CarloSignal/

Background

Monte CarloSignal/

Background

Apply MCCorrection

s

Apply MCCorrection

s

Result

Likelihood

Fit to Data

Likelihood

Fit to Data

Discriminant

Signal

Background

Cross Section

Multivariate

Analysis Technique

Multivariate

Analysis Technique

33

Likelihood Fit to Data•The distribution of the discriminant in data is a

superpositionof the single top and several background template

distributions

Obtain most probable single top content in data by performing abinned 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 andare also allowed to float within their

uncertainties

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

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± = ±1σ shift in bin k

36

Sources of Systematic Uncertainty

Single Top Rate Variations

Shape Variations

Jet Energy Scale

Initial State Radiation

Final State Radiation

Parton Dist. Function

Monte Carlo Generator

Efficiencies / b-tagging SF

Luminosity

Total Rate Uncertainty

10.5% N/A

CDF RunII Preliminary, L=955pb-1

Background

Rate Unertainty

W+bottom 25%

W+charm 28%

Mistag 15%

ttbar 23%

Backgrounds Rate Variations

Shape Variations

Jet Energy Scale

Neural Net b-tagger

Mistag Model

Non-W Model

Q2 Scale in Alpgen MC

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

Likelihood Function Analysis:

Median p-value = 2.3% (2.0)

Matrix Element Analysis:

Median p-value = 0.6% (2.5)

Q L(data | s b)

L(data | b)

Median

More signal likeLess signal like

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

t ch 0.6 0.61.9 (stat) 0.1

0.1(syst)pb

s ch 0.3 0.32.2 (stat)-0.3

0.5 (syst)pb

s+t 0.8 0.81.3 (stat) 0.3

0.2 (syst)pb

41

Likelihood Function Results

t ch 0.2 0.20.9 pb

s ch 0.1 0.10.7 pb

Best fit Separate Search:

s+t 0.3 0.31.2 pb

Best fit Combined Search:

95 s+t channel

Expected 2.9 pb

Observed 2.7 pb

95% upper limit on combined single top cross section

Note: Expected limit assumes no single topCurrent result excludes modelsbeyond the Standard Model

( stSM 2.9 pb)

42

Matrix Element Technique - Result

• Matrix Element analysis observes excess over background expectation

• Likelihood fit result for combined search:

Single Top 2.7 1.31.5 pb

Single Top 2.7 1.31.5 pb

43

Observed p-value

Observed p-value = 1.0% (2.3)

bs+b

CDF RunII Preliminary, L=955pb-1

Observed p-value = 51.3%

CDF RunII Preliminary, L=955pb-1

-2lnQ

44

Central Electron CandidateCharge: -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=0QxEta = 2.91 (t-channel signature)EPD=0.95

Single Top Candidate Event

Jet1

Jet2

Lepton

Run: 211883, Event: 1911511

u,d

d,u

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

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!

Technique s+t cross-section

Expected p-value

Observed p-value

Likelihood Function (955/pb)

0.3(+1.2/-0.3)pb

2.3% 51.3%

Neural Network (695/pb)

0.8(+1.3/-0.8)pb

coming soon coming soon

Matrix Element (955/pb)

2.7(+1.5/-1.3)pb

0.6% 1.0%

Combined Analysis (955/pb)

coming soon coming soon coming soon