Max Baak 1

Top background inVBF H WW (ll)

Max Baak, CERNAtlas CAT top

meeting 8 August ‘08

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Vector boson fusion: H W+W- llVBF H WW (ll)

Clean events: color-coherence between initial and final state W-radiating quarks suppressed hadronic activity in central region

Spin zero Higgs: charged leptons prefer to point in same direction.

Two forward, high-Pt jets from WW fusion process (“tagging jets”).

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Event selection Good muon:

• Muid, pT > 15 GeV/c, ||<2.5 Good electron

• pT > 15 GeV/c, ||<2.5, isEM=0, overlap removal with good muons, R>0.2 Good jet:

• C4Tower, pT > 20 GeV/c, ||<4.8, overlap removal with good muons/electrons, R>0.4

Tagjets:• 1: Highest pT good jet, 2: Highest p good jet + require jj>2.5, m(jj) > 520 GeV/c2

Higgs:• 2 good leptons between tagjets, opposite charge• mT(H) > 30 GeV/c2, m(ll) < 300 GeV/c2

• Basic cuts around the Z mass Event selection

• MET>20 GeV, 2 good leptons, 2 or 3 good jets

No higgs mass dependent cuts Selection not optimized.

Regular cuts ...

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Higgs production x-sections (NLO)

HWW: signifcant discovery potential over wide mass range (>130)

VBF: second significant production mechanism for Higgs at LHC Expect ~ 40 reconstructed Higgs events / fb (@ 170 GeV/c2)

VBF H WW ll gg H WW ll

requirement: lepton → e / mu

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Some interesting facts

“No-lose” theorem applies to W-W scattering: something must show up below mWW < 500 GeV/c2 to avoid unitarity violation.

Main background components• 90% ttbar production, mostly dilepton channel• Some W(W) + jets, QCD & EW

Great synergy with top reconstruction

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Higgs transverse mass mT(H) : calculated like normal transverse mass

• Assume that : m() = m(ll)m(H)TRUE = 170 GeV/c2

tauL : missing momentum

in ztauR : m(ll) = m()sigmaC: missing Et

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Higgs (ll), (ll)

(l

l)

(ll)

WW comes from spin zero Higgs: charged leptons prefer to point in same direction. Define angle in transverse plane ll .

Significant fraction of various backgrounds does not have (anti-)correlated W spins.

Higgs W–W+

l+ l–

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Control sample: ttbar background

ttbar background

Dominant background contribution in VBF H→WW analysis

Extrapolation of di-lepton ttbar background into signal box using b-tag information.

Extrapolation of di-lepton background from semi-leptonic bkg.

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Sample categories

Fit variables: (ll), (ll), mT(H) Higgs events mostly end up in BVeto-sigbox Use other boxes to extrapolate bkg description into BVeto-

sigbox.• Both shapes and normalization

Four possible background sample approximations:• 1 3, or 1 2• 1 3 correction_factor(2/4), or 1 2 correction_factor(3/4)

BTag sample

BVeto sample

sigbox

sigbox

sideband

sideband

(ll) (ll)

(l

l)

(l

l)

1

2

3

4

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Ttbar bkg (ll), (ll) Use BVeto-sigbox for complete background estimate.

• 1 3

• BVeto-sigbox– Projection from BTag-

sigbox– 1 /fb

12

34

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Bkg mT(H)

Leptons not affected by b-tag. Use BVeto-sideband for 1st order background estimate.

• 1 3 correction_factor(2/4)

Smoothed correction factor

12

34

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Bkg mT(H) Projection onto

BVeto-sigbox ...• BVeto-sigbox– Projection from BTag-

sigbox– Projection from BTag-

sigbox, with correction factor

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Ttbar bkg discrimination

Ttbar bkg dominates signal when Bweight > 5

ttbar Higgs

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Extrapolation

Reconstructed transverse mass for selected ttbar events

High Bweight

Medium Bweight

Low Bweight

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Extrapolation Method

Divide Bweight into N domains Fit purest background region, and extrapolate to signal

box.

Signalbox

Background

Bweight

bkg

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Extrapolation into signal box

Purest ttbar sample: fit with distribution f0

BackgroundBweight

Signalbox

p1, q1

p2, q2

p3, q3

p4, q4

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Visual impression of results

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Polynomial Fit results (Note: statistically independent points.) No clear extrapolation curve. Too little statistics for proper extrapolation.

B weight bin

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Conclusion / Plan

Interest in di-lepton ttbar channel as background in VBF H->WW (ll) process.

Plan: acquire better understanding of ttbar di-lepton channel.

Probably get involved in X-sec measurement.

Involvement in new Top reconstruction group Bkg extrapolation techniques B-tagging performance / validation.

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Backup

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Keys pdf Kernal estimation pdf : provides unbinned, unbiased

estimate pdf for arbitrary set of data • K. Cranmer, hep-ex/0011057

E.g. 1-dim keys pdf heavily used in BaBar. I extended this to n-dim keys pdf to model any bkg

distribtion.• To be included in HEAD of RooFit

Automatically includes correct correlations between all observables

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One fit example

mH(true) = 170 GeV/c2

• background• signal +

bkg

Transverse Higgs mass (GeV/c2)

Parameter Value Gl. Corrl. Input

m(higgs) 168 ± 8 12% 170

n(bkg) 90.5 ± 7.4 93% 86

n(higgs;2j) 18.6 ± 5.5 25%

n(higgs;3j) 9.6 ± 7.9 38%

1/fb

ATLAS CSC BOOK

27

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Bkg-only samples

Sig+bkg samplesm(higgs)=180

GeV

Entr

ies

per

bin

2 x 2

Significance determination Generate many pseudo-experiments (using grid):

1. Background-only samples2. Background + signal samples, for various Higgs mass.

Fit each sample with background-only and signal+bkg hypothesis Plot 2 between the fits. Extrapolate fraction of bkg-only sample to fake average signal

sample.

Bkg-only samples faking signal

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Significance results

ATLAS CSC BOOK

Results with 1/fb of data: If higgs mass = 170 GeV/c2 :

• Close to 2.5 sigma signal sensitivity

• 9 GeV/c2 mass resolution.

For mH < 140 GeV/c2, similar sensitivity to gluon fusion analysis.

Background shapes

and normalization obtained fully

from data control

samples.