Top background in VBF H WW (ll)
description
Transcript of Top background in VBF H WW (ll)
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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
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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.