top production and branching ratios

11

Top production and branching Top production and branching ratios ratios

For DØFor DØ collaboration collaboration

Elizaveta ShabalinaUniversity of Illinois at Chicago

Wine and Cheese seminar at FNALWine and Cheese seminar at FNAL09/16/0509/16/05

09/16/0509/16/05 E. Shabalina Joint Theoretical and Experimental Seminar E. Shabalina Joint Theoretical and Experimental Seminar 22

OutlineOutline

• IntroductionIntroduction

• Top pair productionTop pair production– Dilepton channelDilepton channel– Lepton+jets Lepton+jets

•Event kinematics methodEvent kinematics method

•B-tagging methodB-tagging method

• Top branching ratio Top branching ratio

• Top pair production in all hadronic Top pair production in all hadronic channelchannel


• Tevatron was built Tevatron was built more than a decade more than a decade ago to discover top ago to discover top quark successfully quark successfully achieved in 1995 achieved in 1995

• Run I cross sections: Run I cross sections:

– CDF CDF and D0 and D0

• Precision (~25%) was Precision (~25%) was severely statistically severely statistically limitedlimited

Top quark physics todayTop quark physics today• At present At present

– s = 1.96 TeV - 30% higher s = 1.96 TeV - 30% higher production rateproduction rate

– much higher luminositymuch higher luminosity

• Current goal – deliver precision Current goal – deliver precision measurementsmeasurements

• Theoretical prediction of cross Theoretical prediction of cross section – 6.5% accuracysection – 6.5% accuracy

• Tev2000 study: precision of Tev2000 study: precision of ttbar cross section ttbar cross section measurement measurement

• Five W&C seminars since June Five W&C seminars since June 11stst are dedicated to top physics are dedicated to top physics

pbtt 7.14.15.6)(

pbtt 6.17.5)(

1 fb1 fb-1-1 11%11%

10 fb10 fb--

11

5.9%5.9%


Top cross section - motivationTop cross section - motivation• Important test of Important test of

perturbative QCDperturbative QCD• Higher production rate Higher production rate

ttbar resonances ttbar resonances (topcolor) (topcolor)

• Measure in different Measure in different channels channels – Exotic top decays (to Exotic top decays (to

charged Higgs or light charged Higgs or light stop) different cross stop) different cross sections in different sections in different channelschannels

– Dilepton to l+jets cross Dilepton to l+jets cross sections ratio tests sections ratio tests top decays without W top decays without W boson in final state boson in final state

• Measure with Measure with different methodsdifferent methods– b-jet tagging method b-jet tagging method

assumes assumes Br Br (t (t Wb) Wb) = 1= 1• an implicit use of the an implicit use of the

SM prediction: |VSM prediction: |Vtbtb||=0.9990 =0.9990 0.9992 (at 0.9992 (at 90%C.L.) 90%C.L.)

– Topological method Topological method is free from this is free from this assumptionassumption

– Using both test of Using both test of top decays without top decays without bb quark in the final quark in the final statestate

Test of S

tandard

Model


Top productionTop production

• Standard model pair production through strong Standard model pair production through strong interactionsinteractions

• Standard model electroweak productionStandard model electroweak production (single top)(single top)

q-q

~85% ~15%

g-g

Discovered in Run I

To be observed in Run II

= 6.77 ± 0.42 pb for mtop = 175 GeV

=0.88±0.04 pb

=1.98+0.22 pb-0.16


• Very short lifetime Very short lifetime decays as a free quarkdecays as a free quark

• BrBr (t (t Wb) Wb) 100% 100%

… … and decayand decay• W decay modes W decay modes

determine top quark determine top quark final statefinal state

• Dilepton (ee, Dilepton (ee, μμμμ, e, eμμ))– Both W’s decay Both W’s decay

leptonicallyleptonically– BR = 5%BR = 5%

• Lepton (e or Lepton (e or μμ) + jets) + jets– One W decays leptonically, One W decays leptonically,

another one hadronicallyanother one hadronically– BR = 30%BR = 30%

• All-hadronicAll-hadronic– Both W’s decay Both W’s decay

hadronicallyhadronically– BR = 44%BR = 44%

• ττhadhad +X +X– BR = 23%BR = 23%


DDØ detectorØ detectorAll detector subsystems are important for high quality top quark measurements • ElectronsElectrons - energy - energy

clusters in EM clusters in EM section of the section of the calorimeter and calorimeter and track in the track in the central tracking central tracking systemsystem

• MuonsMuons - track - track segments in muon segments in muon chambers and chambers and track in the track in the central tracking central tracking systemsystem

• Jets - Jets - clusters of clusters of energy in EM and energy in EM and hadronic parts of hadronic parts of calorimeter calorimeter


Tuning simulation to dataTuning simulation to data• Monte Carlo simulation is Monte Carlo simulation is

used to calculate selection used to calculate selection efficiencies and to simulate efficiencies and to simulate event kinematicsevent kinematics

• improve the agreement improve the agreement between data and MC:between data and MC:– additional smearing of the additional smearing of the

reconstructed objectsreconstructed objects– correction factors derived correction factors derived

from comparison of Zfrom comparison of Zll ll data and MC events and data and MC events and applied to MCapplied to MC

• Systematic uncertainties – Systematic uncertainties – from uncertainties on the from uncertainties on the smearing parameters smearing parameters and/or from the and/or from the dependence on detector dependence on detector regions, various jet regions, various jet environmentenvironment

SF =

RMS


Electron Electron • Deposit >90% of energy in the Deposit >90% of energy in the

EM calorimeter within a cone of EM calorimeter within a cone of R<0.2 relative to the shower R<0.2 relative to the shower axisaxis

• Isolated: the ratio of the energy Isolated: the ratio of the energy in the hollow cone 0.2 < in the hollow cone 0.2 < R < R < 0.4 to the reconstructed 0.4 to the reconstructed electron energy ≤ 15%electron energy ≤ 15%

• Transverse and longitudinal Transverse and longitudinal shower shapes consistent with shower shapes consistent with those expected for an electronthose expected for an electron

• Reconstructed track found Reconstructed track found within within R< 0.5 from the shower R< 0.5 from the shower position in the calorimeterposition in the calorimeter

• Discriminant combining Discriminant combining information from central information from central tracking system and calorimeter tracking system and calorimeter is consistent with the is consistent with the expectations for a high-pexpectations for a high-pTT isolated electron isolated electron

Electron and muon Electron and muon identificationidentification

MuonMuon

• a muon track segments are a muon track segments are matched inside and outside of matched inside and outside of the toroidthe toroid

• timing (from associated timing (from associated scintillator hits) is within 10 ns scintillator hits) is within 10 ns of the interaction of the interaction muon muon originates from primary vertexoriginates from primary vertex

• a track reconstructed in the a track reconstructed in the tracking system belonging to tracking system belonging to event vertex is matched to the event vertex is matched to the muon candidate found in the muon candidate found in the muon systemmuon system

• Isolated in calorimeter and in Isolated in calorimeter and in the tracking system; isolation the tracking system; isolation criteria are different for criteria are different for dilepton and l+jets analyses dilepton and l+jets analyses

l o o

s e

t i g

h t

l o o

s e

t i g

h t


jet

jet

b

b

p p

E T

Dilepton channelsDilepton channels• Selection Selection

– At least two jets (pAt least two jets (pTT>20 GeV, >20 GeV, |y|<2.5) |y|<2.5)

– Two charged opposite sign Two charged opposite sign leptons (pleptons (pTT>15 GeV; e: |>15 GeV; e: |||<1.1 or 1.5<|<1.1 or 1.5<||<2.5; |<2.5; μμ: |: |||<2)<2)

– Lepton quality: “tight” Lepton quality: “tight” μμ,, “tight” e in ee, “loose” e in “tight” e in ee, “loose” e in eeμμ ( (electron discriminant electron discriminant distribution in data is used to distribution in data is used to extract ttbar signal) extract ttbar signal)

– Large missing ELarge missing ETT in ee and in ee and μμμμ channels; no cut in echannels; no cut in eμμ

• and further selections and further selections are optimized for each are optimized for each channel to account for channel to account for difference in backgroundsdifference in backgrounds

• SignatureSignature

_

EETT

pp


• Drell-Yan Drell-Yan background background rejectionrejection

• ee: ee: – veto events with veto events with

80<M80<Meeee<100<100– >35 GeV(>40 >35 GeV(>40

GeV) for MGeV) for Meeee>100 >100 GeV (MGeV (Meeee<80 GeV) <80 GeV)

• μμμμ::– >35 GeV>35 GeV– is tightened at low is tightened at low

and high values of and high values of azimuthal distance azimuthal distance φφ( ( μμ, , ))

– Remove events with Remove events with φφ((μμ, , )>175°)>175°

BackgroundsBackgrounds• PhysicsPhysics

– Leptons from W/Z decay Leptons from W/Z decay and missing Eand missing ETT from from neutrinos: WW/WZ, neutrinos: WW/WZ, Z/Z/**ll ll

– Estimated from MCEstimated from MC• InstrumentalInstrumental

– jet or lepton in jet fakes jet or lepton in jet fakes isolated lepton (QCD, isolated lepton (QCD, W+jets) W+jets)

– missing Emissing ET T originates originates from resolution effects, from resolution effects, misreconstructed jet or misreconstructed jet or lepton or noise in lepton or noise in calorimeter (Drell-Yan calorimeter (Drell-Yan processes Z/processes Z/**ee(ee(μμμμ) ) (e(eμμ channel is not channel is not affected)affected)

EETT

EETT

EETT

EETT

EETT


BackgroundsBackgrounds• Fake electron (W+jets, Fake electron (W+jets,

QCD events) QCD events) – Fake rate Fake rate from data from data

sample dominated by fake sample dominated by fake electrons (2 loose EMs, electrons (2 loose EMs, low , outside Z mass low , outside Z mass window)window)

– Measure fraction of loose Measure fraction of loose electrons that pass tight electrons that pass tight criteriacriteria

• Fake isolated muon Fake isolated muon (muons from heavy flavor (muons from heavy flavor decays) decays) – Use loose dimuon eventsUse loose dimuon events– One non-isolated muonOne non-isolated muon– Measure probability that Measure probability that

the other is isolatedthe other is isolated• Multiply by number of loose-Multiply by number of loose-

tight events in data tight events in data

• Fake in Z/Fake in Z/** ee( ee(μμμμ) – ) – primary background in ee(primary background in ee(μμμμ) ) channelschannels– spectrum in MC Z events spectrum in MC Z events

agrees well with dataagrees well with data– μμμμ: : directly from simulation directly from simulation – ee: fake rate is measured in ee: fake rate is measured in

+jet events; multiplied by the +jet events; multiplied by the number of data events that fail number of data events that fail the selection but pass all the selection but pass all others in MCothers in MC

22 cut on fit to Z hypothesis ( cut on fit to Z hypothesis (μμμμ))

EETT

EETT

EETT

EETT


eeμμ channel channel• The cleanest channelThe cleanest channel• Optimized to minimize total Optimized to minimize total

errorerror• Optimal cut removes Z/Optimal cut removes Z/**

backgroundbackground

• Extract fake electron Extract fake electron background from the fit to the background from the fit to the observed distribution of observed distribution of electron LH in data electron LH in data

• Shape for real electrons – from Shape for real electrons – from Z Z ee data ee data

• Shape for fake electrons – from Shape for fake electrons – from background dominated sample background dominated sample (anti-isolated muon, low (anti-isolated muon, low missing Emissing ETT))

GeVppHj

jT

lT

lT 122

real electrons

fake electrons


ResultsResults

Background control bin

ttbar signal


Dilepton events propertiesDilepton events properties

Electron likelihood distribution for data events after full selection

eμ

combined

for tt= 7 pb


obs

NN

ifakei

N

iefake

obs

N

exBNxSNLBRNN

fakeeobs )(

1

))()((}),,,,{,(

L

• ee and ee and μμ channels – counting experiments– Define likelihood for each channel based on Poisson probability that

expected number of signal + background events j is compatible with observed Nj

obs

– where

• eμ channel – extended unbinned likelihood method

Cross section calculationCross section calculation

bkgjjjjjj NLBR

,!

),(}),,,,{,( j

obsj

eN

NPLBRNNobsj

Nj

jobsjjjj

bkgj

obsjj

L

bkgphyse NBRLN - electron likelihood

distributions for signal and background events

xi – value of electron likelihood for an electron in each event

bkgphysN - number of physics background

eventsFit simultaneously cross section and Nfake


ee:

Cross sections Cross sections

)(5.0)()(9.7 5.11.1

2.58.3 lumisyststattt

)(1.0)()(8.1 8.11.1


μμ:

)(7.0)()(2.10 6.12.1


eμ:

For dilepton channel combination minimize the sum of negative log-likelihood functions for individual channels

)(6.0)()(6.8 2.10.1


combined dilepton @ m_top = 175 GeV

370 pb370 pb-1-1


Systematic uncertaintiesSystematic uncertainties

Comparable contributions from all sources


Lepton+jets channelLepton+jets channel

• SignatureSignature • SelectionSelection– One isolated lepton One isolated lepton

(p(pTT>20 GeV; e: |>20 GeV; e: |||<1.1 or 1.5<|<1.1 or 1.5<|||<2.5; <2.5; μμ: |: ||<2)|<2)

– At least four jets At least four jets (p(pTT>20 GeV, |y|>20 GeV, |y|<2.5)<2.5)

– >20 GeV and not >20 GeV and not collinear with lepton collinear with lepton direction in direction in transverse planetransverse plane

b

bp p

E T

jet

jet

jet

jet

_

_

EETT


Sample compositionSample composition

Estimate amount of QCD from Matrix Method

Multijet backgroun

d

QCDsig

tightloosesigQCD

NNN

Nloose

Ntight


Discriminant function definitionDiscriminant function definition

,...),(,...),(

,...),(

2121

21

xxBxxS

xxSD

1)(/)(

)(/)(

iiii i

i iiii

xbxs

xbxs

1))(lnexp(

))(lnexp(

ii

i

ii

i

b

sb

s

1))(lnexp(

))(lnexp(

i

ifitted

i

ifitted

bsbs

– probability density functionsfor signal and background a set of input variables

BS ,

,..., 21 xx

normalized distributions of variable i for signal and background

)(),( iiii xbxs

Transform topological variables to be less sensitive to statistical fluctuations in regions of rapid variations

Build logarithm of the signal to background ratios and fit with polinomial

• Only ttbar and W+jets simulated events are used to build discriminant

• Kinematic properties of multijet background are similar to W+jets


Discriminating variablesDiscriminating variables• HHTT – scalar sum of the – scalar sum of the

ppTT of four leading jets of four leading jets • Centrality – ratio of Centrality – ratio of

scalar sum of jet pscalar sum of jet pTT to to scalar sum of jet scalar sum of jet energiesenergies

• AplanarityAplanarity• SphericitySphericity•

• Set of variables is Set of variables is chosen chosen – to provide the best to provide the best

separation between separation between ttbar and W+jets ttbar and W+jets backgroundbackground

– to have the least to have the least sensitivity to the sensitivity to the dominant systematic dominant systematic uncertaintiesuncertainties

• Only 4 highest pOnly 4 highest pTT jets jets are used to build are used to build variables variables

((l,l,EETT))• kkTminTmin==RRjjjj

minminppTTminmin/E/ETT

WW, , RRjjjj

minmin maximum maximum separation between pairs separation between pairs of jets, Eof jets, ETT

WW – scalar sum – scalar sum of lepton pof lepton pTT and , p and , pTT

minmin – – ppTT of the lower p of the lower pTT jet jet

EETT

Linear combination of Linear combination of the eigenvalues of a the eigenvalues of a normalized momentum normalized momentum tensortensor


By construction background peaksat 0, signal – at 1

Discriminant functionDiscriminant function• Fit modeled discriminant function distribution to that of data

• Extract Nttbar, W+jets and multijet events in the sample


Cross sectionCross section• Define Define

where Poisson probability density for where Poisson probability density for nn observed events observed events given given μμii predicted, predicted, ii runs over all bins of the discriminant, n runs over all bins of the discriminant, nii

obsobs – – content of bin content of bin ii as obtained in selected sample as obtained in selected sample

• Expected number of events in bin Expected number of events in bin ii is a function of number of is a function of number of ttbar, W and QCD events in the selected sample:ttbar, W and QCD events in the selected sample:

• f - fractions in bin f - fractions in bin ii of the ttbar, W and QCD discriminant of the ttbar, W and QCD discriminant templatestemplates

• Second term implements Matrix Method constraint on number of Second term implements Matrix Method constraint on number of QCD events via the Poisson probability of the observed number of QCD events via the Poisson probability of the observed number of events in loose but not tight sampleevents in loose but not tight sample


ResultsResults

e+jets

μ+jets )(4.0)()(4.5 2.10.1


)(5.0)()(2.8 9.13.1


e+jets μ+jets

240 pb240 pb-1-1


Results combinedResults combined

)(4.0)()(7.6 6.11.1


For lepton+jets channel combination minimize the sum of negative log-likelihood functions for individual channels

combined @ m_top = 175 GeV

Sample composition:Sample composition:

38% ttbar

44% W+jets

18% multijet background

240 pb240 pb-1-1


Event kinematicsEvent kinematics

Background

dominatedsig

nal

dominate

d


Systematic uncertaintiesSystematic uncertainties

By far the largest systematic uncertainty comes from the Jet energy calibration, 90% of total error


• event has two event has two bb-jets -jets • bb-jets in background -jets in background

processes are seldomprocesses are seldom• Use this feature to Use this feature to

discriminate signal from discriminate signal from backgroundbackground

• Dramatically improves Dramatically improves signal-to-background ratiosignal-to-background ratio

• Signature of a b decay is a displaced vertex – Forms long Forms long lifetime of B-

hadrons (c ~ 450μ) – B-hadrons travel Lxy ~

3mm before decay with large charged track multiplicity

Lepton+jets channel with Lepton+jets channel with bb--taggingtagging

• Use same selection as in topological analysis but – Relax cut on jet transverse

momentum: pT > 15 GeV– Use events with njet3

• Use events with one and two jets as control samples for background estimation

tt

QCD

W+jets


• Reconstructs secondary vertex– 2 tracks with pT1GeV, 1

SMT hit, impact parameter significance >3.5

• Removes tracks associated with K0

S, 0 and photon conversions ( → e+e-)

• Positive tag: – Secondary vertex within a jet

with a decay length significance Lxy/Lxy>7

• Negative tag: – Secondary vertex within a jet

with a decay length significance Lxy/Lxy<7 (due to resolution effects)

bb-tagging algorithm - SVT-tagging algorithm - SVT

Impact parameter


Tagging ratesTagging rates• bb-tagging efficiency-tagging efficiency

– Measured in dijet data Measured in dijet data events for jets with events for jets with muon insidemuon inside

– Compare two samples with Compare two samples with different heavy flavor different heavy flavor content (increased by content (increased by tagging the away jet)tagging the away jet)

– Tag jets with two tagging Tag jets with two tagging algorithms SVT and SLT algorithms SVT and SLT (SLT = soft muon with (SLT = soft muon with ppTT

relrel> 0.7 GeV inside a jet) > 0.7 GeV inside a jet) – Solve system of 8 eqs to Solve system of 8 eqs to

extract semileptonic extract semileptonic bb--tagging efficiencytagging efficiency

– Use MC to correct Use MC to correct measured efficiency to measured efficiency to that for inclusive that for inclusive bb-decays-decays

• Light tagging rateLight tagging rate– Measure negative tagging Measure negative tagging

rate in dijet events (low rate in dijet events (low missing Emissing ETT))

– Correct for long-lived Correct for long-lived particles in light jetsparticles in light jets

– Heavy flavor contribution in Heavy flavor contribution in dijet eventsdijet events

• cc-tagging rate-tagging rate– From MC corrected with the From MC corrected with the

SF derived for SF derived for bb-tagging-tagging


BackgroundsBackgrounds• Calculate QCD (non-Calculate QCD (non-

W) contribution W) contribution from Matrix Method from Matrix Method

• Subtract small Subtract small backgrounds (single backgrounds (single top, VV, Ztop, VV, Z) using ) using known cross known cross sectionssections

• Separate W from Separate W from ttbar using ttbar using difference in their difference in their tagging probabilitytagging probability

• Interpret excess in Interpret excess in observed tagged observed tagged events with events with 3 jets 3 jets over predicted over predicted background as ttbar background as ttbar signal signal

small bkgr


Event tagging probabilityEvent tagging probabilityDØ RunII Preliminary, 363pb-1

W+jets, W+jets, averageaverage

ttbarttbar

≥≥4j, 1 tag4j, 1 tag 4%4% 44%44%

≥≥4j, 2 tag4j, 2 tag 0.4%0.4% 15%15%

• Use MC to calculate event tagging Use MC to calculate event tagging probabilityprobability

• Depends on the flavor composition Depends on the flavor composition of the jets in the final and on the of the jets in the final and on the overall event kinematics overall event kinematics

• Apply the tagging rates measured Apply the tagging rates measured in data to each jet in MC based on in data to each jet in MC based on its flavor, pits flavor, pT and y and y

• For W+jets, use the ALPGEN MC to For W+jets, use the ALPGEN MC to estimate the fraction of the estimate the fraction of the different W+heavy flavor different W+heavy flavor subprocesses.subprocesses.


ResultsResults

≥≥3j, 1tag3j, 1tag ≥≥3j, 2tag3j, 2tag ≥≥4j, 1tag4j, 1tag ≥≥4j, 4j, 2tag2tag

Expect bkgExpect bkg 71 ± 971 ± 9 7 ± 17 ± 1 22 22 ± 3± 3 1.51.5±0.3±0.3

S/BS/B 0.60.6 1.61.6 2.32.3 1212

Observed tagsObserved tags 121121 1111 8888 2121

Background dominated


Kinematics of l+lets tagged Kinematics of l+lets tagged samplesample DØ RunII Preliminary, 363pb-1


Cross sectionCross section

• Define likelihood based on Poisson probability that expected number of signal + background events j is compatible with observed Nj

obs

• The product is taken over 8 independent channels: e/μ +jets, one-/two-tags, 3rd and 4th jet multiplicity bins

• Multijet background in each tagged sample, and the corresponding samples before tagging, is constrained within errors to the amount obtained from Matrix Method

j

jobsjjjjj

bkgj

obsj NPLBRNN ),()},,,,{,( 8,...,1 L


Result and systematic Result and systematic uncertaintiesuncertainties• Gaussian term for each source Gaussian term for each source of errors is included (of errors is included (nuisance nuisance parameter methodparameter method))

• Each source is allowed to Each source is allowed to affect the central value of the affect the central value of the cross section cross section

)(5.0)(1.8 3.12.1 lumisyststattt

Systematic and statistical uncertainties are the same ~ 11%

Main sources:

• JES and jet ID

• B-tagging efficiency in data

• W fractions

• Luminosity

• Combined statistical and Combined statistical and systematic error is obtainedsystematic error is obtained

• Individual Individual contributions are obtained by refitting after fixing all but the Gaussian term under study

363 pb363 pb-1-1


• Probe the assumption Probe the assumption Br(tBr(tWb)=1Wb)=1

• CKM matrix element |VCKM matrix element |Vtbtb|=0.9990|=0.99900.9992 @90% C.L. 0.9992 @90% C.L. R=0.9980R=0.99800.9984. True in SM assuming 0.9984. True in SM assuming

– Three quark generationsThree quark generations– CKM matrix is unitaryCKM matrix is unitary

• For expanded CKM matrix |VFor expanded CKM matrix |Vtbtb|=0.07|=0.070.9993 @90% C.L.0.9993 @90% C.L.• CDF measurement: 162pbCDF measurement: 162pb-1-1

Branching ratio Branching ratio

2222

2

||||||||

||

)(

)(tb

tdtstb

tb VVVV

V

WqtBr

WbtBrR

)(12.1 27.023.0 syststatR


MethodMethod

bbWWtt

lqbWWtt

2 2 bb-jets-jets

1 1 b, b, 1 light 1 light jetjet

2 light jets2 light jetsll qWqWtt

• Split selected sample Split selected sample into 3 categories: 0,1 into 3 categories: 0,1 and and 2 tags2 tags

• Predicted number of Predicted number of ttbar events depends ttbar events depends on Ron R

• Fit R and Fit R and tttt from the from the number of observed number of observed tagged events and tagged events and the event kinematics the event kinematics in 0 tag sample in 0 tag sample

• Compute probabilities to observe 0, 1 and Compute probabilities to observe 0, 1 and 2 tags for each final ttbar state2 tags for each final ttbar state

• Combine to obtain Combine to obtain PPn-tagn-tag(R), n-tag=0, 1, (R), n-tag=0, 1, 2 2 • Use topological discriminant in 0 tag sample with Use topological discriminant in 0 tag sample with 4 jets to determine ttbar content4 jets to determine ttbar content


Fitting procedureFitting procedure

• Perform binned maximum Perform binned maximum likelihood fit to data in likelihood fit to data in – 10 bins of discriminant of 10 bins of discriminant of

l+jets 0 tag, Nl+jets 0 tag, Njetjet44– 2 bins of l+jets 0 tag, N2 bins of l+jets 0 tag, Njetjet=3=3– 4 bins of l+jets 1 tag, N4 bins of l+jets 1 tag, Njetjet=3, =3,

44– 4 bins of l+jets 2 tag, N4 bins of l+jets 2 tag, Njetjet=3, =3,

4 4 – Statistical fluctuations of the Statistical fluctuations of the

multijet background are taken multijet background are taken into account by additional 12 into account by additional 12 Poisson terms (0,1, Poisson terms (0,1, 2 tags, 2 tags, nj=3, nj=3, 4, e+jets, 4, e+jets, μμ+jets) +jets)

• Nuisance parameter method Nuisance parameter method to include systematic to include systematic uncertaintiesuncertainties

NNjetjet=3=3

NNjetjet44


)(03.1)(

)( 19.017.0 syststat

WqtBr

WbtBr

ResultResult

)(5.0)(9.7 7.15.1 lumisyststattt

Statistical uncertainty dominates

230 pb230 pb-1-1

Potential for improvement – include dilepton events


All hadronic channelAll hadronic channel• Signature: 6 jets, 2 Signature: 6 jets, 2 bb--

quark jetsquark jets• All decay products All decay products

should be visible in the should be visible in the detector, no energetic detector, no energetic neutrinos produced neutrinos produced

• Six jet multijet Six jet multijet production rate is production rate is many orders of many orders of magnitude larger than magnitude larger than ttbarttbar

• Impossible to extract Impossible to extract signal without tagging signal without tagging bb-jets – SVT algorithm -jets – SVT algorithm is used is used

• NNjetsjets 6, p 6, pTT>15 GeV>15 GeV• Suppress multiple Suppress multiple

interactions (second interactions (second interaction is also interaction is also hard QCD process): hard QCD process): – Reject events with Reject events with

several hard primary several hard primary vertices >3 cm apartvertices >3 cm apart

– At least 3 jets assignedAt least 3 jets assigned– Jet is assigned to PV if Jet is assigned to PV if

at least 2 tracks from it at least 2 tracks from it come from PVcome from PV

– Removes 32%Removes 32%• Reject bb background:Reject bb background:

– R(tagged jets)>1.5R(tagged jets)>1.5


TRF and neural networkTRF and neural network• Derive TRF (tag rate Derive TRF (tag rate

function) in the 6-jet function) in the 6-jet data sample (ttbar data sample (ttbar contribution is ~0.3%) contribution is ~0.3%) in 4 bins of Hin 4 bins of HTT: 0: 0200, 200, 200 200 300, 300 300, 300 400, 400, 400 GeV400 GeV

• Parameterize as a Parameterize as a function of jet pfunction of jet pTT, , γγ, , φφ, , position of primary position of primary vertex along the beamvertex along the beam

• Compare predicted and Compare predicted and observed tagging rates observed tagging rates and obtain correction and obtain correction factorfactor

• Select a set of Select a set of variables variables discriminating signal discriminating signal from backgroundfrom background– Avoid as much as Avoid as much as

possible JES dependent possible JES dependent variablevariable

– Use smallest possible Use smallest possible number of input number of input variables variables

• Combine into Neural Combine into Neural network network


Discriminating variables

• Energy ScaleEnergy Scale – H – HTT

• Event ShapeEvent Shape – aplanarity – aplanarity• Soft non-leading JetsSoft non-leading Jets – E – ET56 T56

– geometric mean of the – geometric mean of the transverse energies of the transverse energies of the 55thth and 6 and 6thth leading jet leading jet

Variables are designed to address different aspects of the background • Rapidity –Rapidity – < <22> - weighted > - weighted

RMS of RMS of of 6 leading jets of 6 leading jets • Top Properties – Top Properties –

– MMminmin3,43,4 – the second smallest – the second smallest

dijet massdijet mass– Mass likelihood, Mass likelihood, 22-like variable -like variable

calculated from Mcalculated from MWW, , WW, , toptop


Cross section calculationCross section calculation

• Background was estimated on the Background was estimated on the sample containing signal correct sample containing signal correct cross sectioncross section

TRFTRF – probability to tag ttbar MC event using TRF

btagbtag – probability to tag ttbar event – probability to tag ttbar event using using b,cb,c and light tagging rates and light tagging rates

)1(btag

TRFtt

TRFobstt

L

NN

TRFTRF

0.125±0.00.125±0.00202

btagbtag 0.60±0.010.60±0.01

TRFTRF/TRFTRF 0.207±0.00.207±0.00404

NNobs obs = 541= 541 NNTRF TRF = = 494±8494±8

nn>0.nn>0.99


Cross section and uncertaintiesCross section and uncertainties

)(3.0)()(2.5 5.10.1


At mAt mtoptop = 175 GeV, 350 pb = 175 GeV, 350 pb-1-1

JES error dominates – 70% of total systematic error

)()(7.10.8 3.32.2 syststattt

CDF 311 pb-1 :

~40% relative error

~55% relative error

Potential for Potential for improvement: make improvement: make better use of double better use of double tagged eventstagged events


SummarySummary

)(4.0)(7.0)(6.01.7 lumisyststattt CDF combined up to 350 pb-1 ~13% relative error

Accepted for publication in PLB

Work in progress on combination of the latest results up to

370 pb-1

Best precision: ~16% l+jets/btag at

363 pb-1


From TeV2000 to reality From TeV2000 to reality

• Do we meet Do we meet expectations?expectations?

• For 363 pbFor 363 pb-1-1: : – predicted – 180 predicted – 180 bb--

tagged events tagged events (scaled from 500 (scaled from 500 per fbper fb-1-1))

– Observed – 140 Observed – 140 (241 tagged event, (241 tagged event, 101 – expected 101 – expected background) background)

• Can we do better?Can we do better?– Data qualityData quality

• Improved calorimeter Improved calorimeter calibrationcalibration

• Improved performance of Improved performance of SMT is crucialSMT is crucial

– Improved simulationImproved simulation– OptimizationOptimization– Better tools Better tools

• Neural network lifetime Neural network lifetime b-taggerb-tagger

– Fighting major sources of Fighting major sources of systematic uncertaintiessystematic uncertainties


Glance into the futureGlance into the future• Assumptions:Assumptions:

– Errors from limited MC Errors from limited MC statistics are set to 0statistics are set to 0

– Luminosity dependent Luminosity dependent and constant terms:and constant terms:• JESJES• B-tagging efficiencyB-tagging efficiency• Lepton identificationLepton identification

• Limiting factors: Limiting factors: – Luminosity (6.5%)Luminosity (6.5%)– Heavy flavor fractions Heavy flavor fractions

(5.9%)(5.9%)• Solutions:Solutions:

– Measure ratio of ttbar to Measure ratio of ttbar to W+jets cross sectionW+jets cross section

– Combine channels Combine channels

This will be replaced by a real

plot

Total error on l+jets/btag Total error on l+jets/btag channelchannel


ConclusionConclusion

• The precision of the latest top pair production The precision of the latest top pair production cross section measurements rapidly cross section measurements rapidly approaches accuracy of theoretical prediction approaches accuracy of theoretical prediction and will allow to probe Standard Modeland will allow to probe Standard Model

• With combination of measurements in With combination of measurements in different channels and using different different channels and using different methods we have an excellent opportunity to methods we have an excellent opportunity to exceed the precision limit set by TeV2000 – exceed the precision limit set by TeV2000 – 11% for 1 fb11% for 1 fb-1-1

• … … and the one for 10 fband the one for 10 fb-1-1 – 5.9% – but – 5.9% – but with less luminosity!with less luminosity!

This is a challenge. Let’s go for This is a challenge. Let’s go for it!it!

top production and branching ratios

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