measurement of the top quark pair production cross section...

Measurement of the Top Quark PairProduction Cross Section using

a Topological Likelihood and using Lifetime b-Tagging

page page 11Tobias Golling

Tobias Golling - SCIPP - August 24th 2004

with the DØ Detector in Run II at the Fermilab Tevatron at a center of

mass energy of √s = 1.96 TeV

Overview

Physics Motivation Tevatron & DØ Top Quark Production and Decay Overview over the Analyses Methods

1) Common W+Jets Preselection

2a) Topological Analysis 2b) Lifetime b-Tagging Analysis

Summary & Outlook


tt

tt--

WW++

WW - -

bb

bb--

qq

q'q'

µ

µ

pppp --

The Top Quark in the Standard Model


Why is the Top Quark so interesting ? completes the quark sector large mass m

top ~ 180 GeV / c2

short lifetime ~ 5 · 10-25 s sensitive to physics beyond the Standard Model

Discovery of the Top Quark in 1995by the CDF and DØ Collaborations.

Top-antitop production cross section test of perturbative QCD sensitive to

top decays to New Physics particles (e.g. charged Higgs) Physics beyond the SM decaying to top-antitop

Higgs-Boson coupling to fermions: f ~ m

f

t ~ 1



1

Corrections to W and Z boson mass from top quark and Higgs boson loops allow prediction of the top quark and the Higgs boson mass

Comparison of precision EW measurement ↔ corrections⇒ prediction of top quark mass

zero order

discoveryobservation



corrections to W and Z boson massfrom top quark and Higgs boson loopsallow prediction of the top quark and the Higgs boson mass

corrections: ~ Mt

2

corrections: ~ ln (MH)

Goal for Tevatron in Run II:M

t = 3 GeV

MW

= 20 MeV

The Tevatron at Fermilab


Main Injector & Recycler

Tevatron

Booster

p p

p source

Chicago

_

_

CDF DØ

p p

√s =1.96 TeV ∆t = 396 ns

Run I 1992-95 Lint

~ 125 pb-1

Run II 2001-09(?) 40 × larger dataset at increased energy

Only place to study the top Only place to study the top

quark before the LHC eraquark before the LHC era

DØ Detector at Fermilab


new silicon and fibre tracker new ~2 T solenoid

upgraded muon system upgraded trigger/DAQ

The Silicon Microstrip Tracker


axial, double-sided small-angle stereo and double-sided 90° detectors 800k channels, SVX2 readout

Silicon Microstrip Tracker (SMT): 6 barrels, 16 disks tracking out to ~ = 3

Data Set


analyseddata

installation finishedand detector

commissioning Analysed data setin Run I (1992-1995):

~ 125 pb -1

Analysed data setin Run II (08/2002-10/2003)for winter and summer '04

conferences:~ 140 - 165 pb -1

Data set in Run II until 2009:

~ 4-9 fb -1

N = σ ∙ ÿ∫ L dt

Run I record

Top Quark ProductionTop quarks mainly produced in pairs at Tevatron and LHC


Tevatron LHCtypical S/B: ~1/1 10-100

qq gg

ttbarNLO (pb) qq->tt gg->tt

Run I (1.8 TeV) 4.87±10% 90% 10%

Run II (2.0 TeV) 6.70±10% 85% 15%

LHC (14 TeV) 803±15% 10% 90%

σtt

Run I:

ttbar=5.7±1.6 pb

(statistics limited)

Top Quark Decay

lepton

neutrino

b -jet

b -jet

jetjet


τ+Xµ+jetse+jetse+ee+µµ+µhadronic

1.4 % 1.4 %2.8 %

14.8 %

14.8 %

22 %44 %

Top quarks decay predominantlyTop quarks decay predominantly

(~100%) to a W-Boson and a b-quark(~100%) to a W-Boson and a b-quark

Top-Antitop SignaturesTop-Antitop Signatures

determined by the W decay modes:determined by the W decay modes:

`dilepton channel'`dilepton channel'

~5% :~5% : 2 jets, 2 charged leptons, 2 neutrinos2 jets, 2 charged leptons, 2 neutrinos

`lepton+jets channel'`lepton+jets channel'

~30%:~30%: 4 jets, 1 charged lepton, 1 neutrino4 jets, 1 charged lepton, 1 neutrino

- Large statistics compared to dilepton channel- Large statistics compared to dilepton channel

- Clear signature compared to all-jets channel- Clear signature compared to all-jets channel

`all-jets channel'`all-jets channel'

~40%:~40%: 6 jets 6 jets

always 2 jets are b-jetsalways 2 jets are b-jets

I will concentrate on theI will concentrate on the

'muon+jets channel''muon+jets channel'


Analyses Overview – 2 Methods

QCD

W+jetstt-

QCD

W+jetstt-

QCD

W+jets

tt-

Topological Likelihood Method: Isolate and estimate signal content, fit ttbar fraction by shape comparison

Apply Lifetime b-Tagging(Counting Experiment): Further enhance signal content, subtract estimated backgrounds

Common W+jets selection(W decays leptonically to muon or electron) Enhance signal content

1. 2.


Hard scatter primary vertex

Isolated high p

T lepton

Neutrino reconstructedas missing transverse energy

≥ 3 jets with p

T > 15 GeV

and || < 2.5

Signature - W+Jets Selection

tt

WW++bb

tt--

WW - - bb--

qq

q'q'

ℓ

ℓ

pppp --


Jet Reconstruction and IdentificationThe jet reconstruction and identification efficiency in data is notreproduced by the MC: DØ identifies jets in the calorimeter only (cone-algorithm) Determine efficiency in data and in MC and determine MC correction factor Use pT-balanced physics processes

q

q-

, Z, J/,

tracks calorimeter cluster

}}jet and (Z, J/, ): back-to-back in balanced in p

T

not balanced in due to z-boost

Efficiency determination: Select the away-object (,Z, J/, ) Select jet reconstructed only from tracks back-to-back to (Z, J/, ) check how often an associated calorimeter jet is found

gjet

Muons are reconstructed in the muon system and in the tracking systemMuons coming from a leptonic decay of a W boson tend to be isolated from jets have a relatively high transverse momentum


Muon Isolation

R=22Loose muon isolation:

R , je t 0.5

Tight muon isolation:

E Tcalorimeter cells R0.4−E T

calorimeter cells R0.1

P T 0.08

E Ttracks R0.5

P T 0.06

Muon = MIP(Minimum Ionizing Particle)

1. Topological AnalysisStatus Moriond 04


Instrumental Background (fake lepton, fake MET):QCD multijet production

Electron Fakes:Electrons faked by (electromagnetic) jets

Muon Fakes:Muon-fakes are real muons which are fakely isolated(muons from semileptonic b-decays, where the b-jet is not reconstructed)


Backgrounds

Physics Background:Electroweak W production W ℓ+

ℓ

additional ≥ 4 jets from ISR

W+1jet W+2jets ...

MET Fakes:Misreconstructed calorimeter energy

Solve linear system of equations for NQCD

and NW+ttbar

tight lepton:

Nloose

= NQCD

+ NW+ttbar

Ntight

= QCD

* NQCD

+ W+ttbar

* NW+ttbar

QCD

= 8% W+ttbar

= 82%

QCD

W+jetstt-

QCD

W+jetstt-

Determine instrumental QCD multijet background purely from data


loose lepton:

Determination of QCD Background

Topological Analysis OverviewQCDW+jets

QCD

QCD W+jetsW+jets

loose Wselection+ ≥ 4 jets

tight Wselection+ ≥ 4 jets

DetermineQCD

Background

Combine topological event information in a likelihood discriminant, and perform a fit to the data

tt-

tt-

tt-

tt-


Define variables which describe the event topologyCriteria: - Good separation power - Low sensitivity to the jet energy scale (dominant systematic uncertainty)

WW++qq

q'q'

qq q'q'µ µ --qqqqtt

tt--

WW++

WW - -

bb

bb--

qq

q'q'

µ

µ

pppp --

Jets: high-energetic isotropic

Jets dominated by QCD-Bremsstrahlung: low-energetic in the forward region


ElectroweakW production

ttbar signal

Event Topology


Topological Variables

Sphericity

AplanarityKTmin

'

HT2'

Variables describing theangular distributions ofthe physics objectsin the final state:

Sphericity Aplanarity

Ratios of energy dependent variables:

HT2' (describes centrality)

KTmin

' (=Rmin

(jet,jet)·ETmin/E

TW)

Si = ttbar signal

Bi = W+jets background

i runs over the 4 topological variables

L D=∏ i S i

∏ i S i∏ i B i


Likelihood Discriminant (LD)

Describe data by a linear combination of ttbar, W+4jet and multijet (QCD)

Constrain QCD by using the loose and tight system of equations

Fit the relative fractions


Fit linear combination of QCD (inverted tight selection in data), W+4jet and ttbar to data

Likelihood Fits for µ+Jets & e+Jets

µ+jetsL=144pb-1

e+jetsL=141pb-1

Likelihood Discriminant Likelihood Discriminant


Kinematic Distributionse+jets

µ+jets


Dominant uncertainties: Jet energy scale Jet reconstruction efficiency

ResultComparison with Run I & Summer 03

p p t t Xl jets =7.2−2.4

2.6 stat −1.71.6 syst ±0.5 lum i pb

p p t t X jets =6.0−3.0


p p t t Xe jets =8.8−3.7


=N t t

BR⋅L⋅selBR=0.44

sel=10%L=143 pb−1

Run I summer 03

sum m er 03

sum m er 03


Summer 03 NOW:Large improvement instatistical and systematicuncertainties

Outlook: Publication this fall L = 230 pb-1 Improved systematics

Summary of Topological Analysis& Outlook

2. Lifetime b-Tagging AnalysisStatus ICHEP 04



Count the number of tracks with Count the number of tracks with large positive DCA significancelarge positive DCA significance Jet is tagged if Jet is tagged if NN

trackstracks((>2) > 3 or>2) > 3 or

NNtrackstracks

((>3) > 2>3) > 2

Explicitly reconstruct vertices whichExplicitly reconstruct vertices which are significantly displaced from theare significantly displaced from the primary vertexprimary vertex VV 00 Filter: Filter: Remove track pairs in the mass Remove track pairs in the mass windows corresponding to Kwindows corresponding to K

SS, ,

and photon conversions (and photon conversions ( -> e -> e++ee--))

Counting Signed Impact Parameter (CSIP)Counting Signed Impact Parameter (CSIP) Secondary Vertex Tagger (SVT)Secondary Vertex Tagger (SVT)

B-Tagging at DØ

I will concentrate on the SVT resultsI will concentrate on the SVT results

DCA

Secondary VertexPrimaryVertex

(DCA = Distance to Closest Approach)


Estimate QCD background purely from data:using the loose and tight system of equations(Can be done before or after tagging)

Backgrounds

Subtract small backgrounds using known cross sections: Diboson production: WW l+jets, WZ l+jets, WZ jjll, ZZ lljj Single top production in s- and t-channel Z l+jets

Main background: W+jets Use W+jets sample generated with ALPGEN and interfaced to PYTHIA Rely on ratios of the cross sections of the generated sub-processes:

W+light W+c W+cc W+b W+bb

Apply a matching procedure of partons from the matrix element generator and reconstructed jets

eliminate double counting of the exclusive processes reduce sensitivity to parton generation cuts

tightloose


Philosophy of this B-Tagging Analysis

N tag=N untagged×P eventtag

P eventtag n≥1tag =1−∏ jets 1− jet flavor E T ,

Present versions of DØ MC do not reproduce the b-tagging efficiency and mistag rate observed in data

Tagging algorithm not applied in MC

Instead: Measure jet tagging efficiencies in data (for jet flavor = b,c,light) Parameterize as a function of jet E

T and :

jet(flavor)(E

T,)

Apply tagging parameterisations jet(flavor)

(ET,) to the Monte Carlo

Derive event tagging probabilities: Ptagevent

Determine number of expected tagged events in the MC: Ntag

In particular for the W+njets background:

P W njetstag =∑flavor F flavor P W njets flavor

tagF flavor P W b b j j

tag =42%

P W j j j jtag =1%


Estimate ttbar production cross section from observed excess in the actual number of tagged events with 3 and ≥4 jets with respect to the background expectation Optimum use of the statistical information:

single tags and double tags

Visualize B-Tag Philosophy

P t ttag=45% P t t

double tag=14%


Challenge: Select pure sample of b-jets in data

Enrich dijet data sample in bbbar QCD heavy flavor production:Select jets which contain muons (muon-in-jet)

2 Methods: Use Shape of P

Trel to discriminate

between heavy and light flavor P

Trel = transverse momentum of the

muon relative to the (µ+jet) axis

Apply Lifetime Tagger and Soft Lepton Tagger to two samples (purely from data) muon-in-jet muon-in-jet with away jet lifetime tagged

(further enriched in heavy flavor) Clearly solvable system of 8 equations with 8 unknowns

B-Tagging Efficiency

jet PTrel

µ

µ+jet

B-Tagging Efficiency in Data


Mistag Efficiency +light flavor

Mistag Efficiency = Tagging efficiency for light flavor jets “Mis-Tagging” not due to lifetime but due to decay length resolution

ligh t flavor+ =SF heavy flavor×SF long lived×

-

Idea: Measure negative tagging efficiency -

in a multijet data sample Correct for heavy flavor jets SF

heavy flavor

(which have a higher negative tagging rate than light flavor jets) Correct for the remaining long lived particles which are not present in negative tagged jets SF

long lived

Primary Vertex

JetTracks

SecondaryVertex

SecondaryVertex

positive tag negative tag

µ-

IP

SV

Jet 1

IP

SV

Jet 2MTC

mip signalmip signalin calorimeterin calorimeterr


IP

SV

Jet 1

Muon+Jets Candidate Event

Jet 3

Jet 4

Jet 5


Result for SVT Tagger

=8.2−1.21.3 stat −1.6

1.9 syst ±0.5 lum i pb

Dominant systematic uncertainties: Jet energy scale Jet reconstruction efficiency b-tagging efficiency in data

Single Tags: Double Tags:

Luminosity =164 pb-1

Control bins


Summary & Outlook

Topological Analysis: World best systematics for a topological analysis Forthcoming publication with 230 pb-1

B-Tagging Analysis: Just got ready for ICHEP Forthcoming publication with improved systematics

Understanding of data set:Understanding of data set:jets, jets, leptonsleptons, met, b-tagging, triggering,..., met, b-tagging, triggering,...Development of powerful high massDevelopment of powerful high massanalysis techniques analysis techniques Prepared for Prepared for LHC LHC

Direct input toDirect input tovarious analyses...various analyses...


R=B(tWb)/B(tWq) Measurement

In the SM, unitary of the CKM matrix:In the SM, unitary of the CKM matrix: R=∣V tb∣

2

∣V tb∣2∣V ts∣

2∣V td∣2=∣V tb∣

2

R=0.7−0.240.27 stat −0.10

0.11 syst

R=0.65−0.300.34 stat −0.12

0.17 syst

SVT:SVT:

CSIP:CSIP:

CSIPCSIP

CSIPCSIP

SVTSVT

SVTSVT

tttt versus R (R fixed in the fit) versus R (R fixed in the fit)

68% and 90% CL contours68% and 90% CL contours

Good agreement with SM: R=1Good agreement with SM: R=1

R deduced from the numberR deduced from the numberof single tagged and the of single tagged and the number of double tagged eventsnumber of double tagged events


Top Mass MeasurementMeasurements in l+jets channel (~150 pbMeasurements in l+jets channel (~150 pb-1-1))- - template methodtemplate method uses templates for signal and background mass spectra uses templates for signal and background mass spectra- - ideogram methodideogram method uses analytical likelihood for event to be signal or uses analytical likelihood for event to be signal or background – for each selected eventbackground – for each selected event

template methodtemplate method

ideogram methodideogram method

systematics limited:systematics limited:- jet energy scale- jet energy scale- ttbar modeling- ttbar modeling- W+jets modeling- W+jets modeling- ...- ...

W-

W0

sum


Helicity of the W in ttbar Events

FF++

Backup Slides


Probability for a jet to be tagged conveniently broken downin two components: Probability for a jet to be “taggable” (=“taggability”) Probability for a taggable jet to be tagged (=“tagging efficiency”)

Motivation:Decouple tagging efficiency from issues related to

tracking efficiency calorimeter noise

which are absorbed in the taggability

Taggability: Probability to match (R

Challenge: Select pure sample of b-jets in data

Enrich dijet data sample in bbbar QCD heavy flavor production:Select jets which contain muons (muon-in-jet): b (->c) -> µ (b-jets) c -> µ (c-jets) /K -> µ (light flavor jets)

PTrel

= transverse momentum of the

muon relative to the (µ+jet) axis

PTrel

discriminates between b-jets and non-b-jets

Templates: Shape of P

Trel distribution for c- and b-jets from MC

Shape of PTrel

distribution for light flavor jets from data

(random tracks in a jet)

jet PTrel

µ

µ+jet


B-Tagging Efficiency in Data

1.) ST vs. NT (Single Tag vs. No Tag): PTrel

Fit

Derive b-fraction in data from PTrel

fit to the templates

before and after lifetime tagging the muon jet:

2.) DT vs. ST (Double Tag vs. Single Tag): PTrel

Fit

Same as 1.) but require that away-jet is lifetime tagged to further enrich the sample in b-jets:

3.) System8: Solely from data 2 samples with different b-fractions

muon-in-jet muon-in-jet with away jet lifetime tagged

2 tagging algorithms soft muon tagger SVT tagger

Solvable system of 8 equations: before and after tagging with any or both of the taggers

b−data =

N − jettag F b−

tag

N − jet F b−

b−data =

N Dtag F b−Dtag

N away− jettag F b−

tag

use Method 3. fo r b−data in c l

data=inc lM C b−

data

b−M C


B-Tagging Efficiency in Data – 3 Methods

ttbar MC:


inc ldata=in c l

M C b−data

b−M C

Inclusive b-Tagging Efficiency

Method 1: Determine N

QCD in untagged

sample (topological approach)

Event tagging Probability PQCD

from data

NQCD

tag = PQCD

NQCD

Caveat:P

QCD has to be determined on

sample with same heavy flavor fraction same kinematicsas the signal sample

Method 2: Determine N

QCDtag directly in

tagged sample (topological approach)

Caveat: Large statistical uncertainty One has to verify that

QCDtag =

QCD

large statistical uncertainty


QCD Background Determination

Use W+jets samples generated by ALPGEN interfaced to PYTHIA Don't use absolute values of cross sections, but rely on their ratios Apply matching procedure between Matrix Element partons and reconstructed jets (à la Mangano)

reduce double counting and sensitivity to parton generation cuts

Remarks: Use matching of Matrix Element partons and parton-jets in the future LO estimate of Wbb / Wjj reproduced by NLO ? Qualified “yes” for scale choices >= 50 GeV and pT cuts of about 15 GeV or greater


Flavor Composition of W+Jets


Kinematic Distributions of Tagged Events


Result for CSIP Tagger

=7.2−1.21.3 stat −1.4

1.9 syst ±0.5 lum i pb

Dominant systematic uncertainties: Jet energy scale Jet reconstruction efficiency b-tagging efficiency in data

Single Tags: Double Tags:

Luminosity =164 pb-1

Control bins


DØ Tracking System


Particle Identification at DØ

Had.Layers

Tracking Systemwith magnetic field

Calorimeterinduces showersin dense material

InnerSilicon

Detectors

Muon Detectors

InteractionPoint

Absorber Material

Curvature > Momentum

Electrons

Quarks and Gluons

Muons

Jets

EM Layers

measurement of the top quark pair production cross section...

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