Standard Model Higgs Searches at D Ø

Suyong Choi (SKKU) SUSY 2008 1

Standard Model Higgs Standard Model Higgs Searches at DØSearches at DØ

Suyong ChoiSKKU, Korea

for DØ Collaboration

Suyong Choi (SKKU) SUSY 2008

Suyong Choi (SKKU) PASCOS 2006, Columbus, Ohio

2

Higgs in the Standard Model1. Mass of elementary particles

– coupling to massive particles stronger2. Electro-weak symmetry breaking

Higgs in the Standard Model1. Mass of elementary particles

– coupling to massive particles stronger2. Electro-weak symmetry breaking

Particles of the Standard Model


Limits on MLimits on MHH

• Current limits– Direct searches at LEP

MH > 114 GeV @ 95% CL

– Fits to electroweak dataMH < 160 GeV @ 95% CL

– MH <190 GeV if direct search result included

Light Higgs favored

• Tevatron– Direct Searches : rule out

or find evidence– Precision mt and MW

measurements

MH=87+36-27 GeV


SM Higgs Production at the SM Higgs Production at the TevatronTevatron

• Though Higgs production copious, not all channels are accessible

gg→H– Useful for MH>140 GeV

– H→WW→ll– Background: WW

qqW/Z+H– MH<140 GeV

– WH→lbb

– ZH→llbb, bb

– Background: W+bb, Z+bb, top

• Sensitivity studies have shown that all channels must be studied, CDF + D0 combination is essential

pb


Tevatron Collider StatusTevatron Collider Status

• Excellent performance– Steady

increase in instantaneous luminosity

– >85% data collecting efficiency

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Results presentedtoday are based on1.0 ~ 2.3 fb-1 of data

4.3 fb-14.3 fb-1

3.7 fb-13.7 fb-1


W Associated Production W Associated Production WHWH→→bbbb

• Sensitive for MH<140 GeV– Large x Br

6

q

'q

*W

W

H b jets

b jets

Two b-jets form a resonance


WH SearchWH Search

• Single and double b-tagged jet samples are analyzed separately and optimized

193 observed204 31 expected2.3 WH expected

Expected signal x 10


Neural Network Neural Network SelectionSelection

• Variables for ANN– pT of two jets

– Opening angle of jets

– Dijet system pT and mass

– pT (lepton +)

• Observation in agreement with background only hypothesis


WH Search ResultsWH Search Results

• No excess observed Set limits on cross section x Br

• Limits obtained by fitting the NN output– ST and DT treated as independent channels

• Systematics

9

Expected limitsSources Error

Luminosity 6%

Trigger 3~10%

Jet energy scale 2~6%

B-tagging 4~6%

Background x-section

16~20%


WH Search ResultsWH Search Results



Z Associated Production Z Associated Production ZH→ZH→++--bbbb

• Clean• Small cross section x Br

– MH<140 GeV

q

q

*Z

Z

Hb jets

b jets


Neural Network AnalysisNeural Network Analysis• No significant excess• Set limits on x Br

– NN output distributions– Systematic errors and

correlations considered

• Systematics

– Background error : 28%– Signal eff. error : 8%

Sources Error


B-tagging 7%


Higgs Limits from Higgs Limits from ZH→ZH→++--bb Analysisbb Analysis


ZHZH→→bbbb

• Advantage of large branching fraction of Z→ – MET + 2 jets – cannot reconstruct Z explicitly– Large multijet background– Recovers leptonic decays of WH and ZH, where leptons were

not reconstructed explicitly


Missing EMissing ETT + jets + jets

• Data: 2.1 fb-1

• MET > 50 GeV

• Jets– 2 or 3 jets, pT>20 GeV

– 2 leading jets should not be back-to-back

• W+jets and multijets dominant– Multijet background due

to mismeasured jet ET

signal x 500


Multivariate AnalysisMultivariate Analysis

• Boosted decision tree result using 26 variables after b-tagging


ZHZH→→bb Search Resultbb Search Result

• Systematic uncertainties

• Limits– best limit in W/Z+H

Sources Error

Luminosity 6.1%

Trigger 5.5%


B-tagging 6%

Background x-section

6~16%

Heavy flavor fractions

50%


H→WW→H→WW→++--• Important for mH>140 GeV

– Final state: 2 leptons + MET– Cannot reconstruct MH

• Data: 2.3 fb-1

– 1.1 (IIa data) + 1.2(IIb data)– ee, e,

• Selection– 2 oppositely charged leptons – Large MET– Di-lepton mass– min( MT(e), MT() ) – H T

reduce Z, W+jets, tt-bar

• Analysis optimized for each MH

Signal

1.1fb-1


NN AnalysisNN Analysis

• Preselection • After final selection


H→WWH→WW

• Factor 2.4 away from SM For MH=160 GeV

• Systematic uncertainty

• Combine distributions from different channels– Statistical uncertainty +

Correlated systematics

Sources Error

Trigger 5%


Muon momentum resolution ()

11%

Muon ID efficiency -5% +8%

WW x-section 4%


Other SM Higgs SearchesOther SM Higgs Searches

• WHWWW– 3 lepton final state– Recovers sensitivity

MH ~ 140 GeV

• H – Not a discovery

channel at the Tevatron

– Analysis with less model dependence

22


Combined DØ SM Higgs Combined DØ SM Higgs ResultsResults

• Correlations of systematic errors taken into account


Combined CDF and DØ Combined CDF and DØ ResultsResults

24

Factor 1.1 away!


Prospects for MProspects for MHH<140 <140 GeVGeV

• We achieved 1.7 factor improvement in sensitivity since 2005– not including gains due to lumi

• We expect additional x2 gain in sensitivity– Optimized b-tagging with inner silicon Layer 0– semileptonic b-tags– dijet mass resolution– lepton efficiencies– refined multivariate analyses

25


Prospects for MProspects for MHH>140 >140 GeVGeV

• We achieved 1.7 factor improvement in sensitivity since 2005 (not including gains due to lumi)

• We expect additional x1.4 gain in sensitivity– lepton efficiencies– multivariate analyses

26


Expected Higgs Expected Higgs Sensitivity in 2009/2010Sensitivity in 2009/2010

• Assuming 2 experiments

27

2009

20102009


SummarySummary

• We searched for Standard Model Higgs boson in all the sensitive channels using the DØ data

• Results in agreement with expected backgrounds

• In 2008, we may be able to exclude new MH range beyond that of LEP– CDF+D0 results combined

• Many improvements expected to raise sensitivity in a broad range of MH - most exciting years to come!


Standard Model Higgs Standard Model Higgs SearchesSearches

WH

ZH

H→WW


The DØ DetectorThe DØ Detector• Tracking

– Precision silicon vertex detector– Scintillating fiber tracker– 2T B-field

• Calorimetry– Liquid Argon-Uranium– ||<4– Excellent linearity and

resolution

• Muon detector– Low punch throughs– 1.8T toroidal B-field

• Trying to exploiting full capabilities


Search for WHSearch for WH→→bbbb

• Data: 1.7 fb-1 with e and

• Event preselection– lepton: pT>15 GeV

– Missing ET: ET>20 GeV

– 2 Jets: pT>20 GeV

||<2.5

• Veto on– Additional high pT track

– 4th jet

• Background is well understood


Tevatron @ FermilabTevatron @ Fermilab

• @ s=1.96 TeV

• Circumference 6 km

• The only place to directly look for Higgs and supersymmetric particles until LHC

pp


b-jet Taggingb-jet Tagging

• D0 developed sophisticated Neural Network based algorithm– Lifetime of a b hadron is

quite long (a few mm)– Superb efficiency– Samples and performance

derived from data– Fakes are due to finite

resolutions of the tracking detector

Detector view

– Decay length– Impact parameter– Measurement errors

Primaryvertex

Secondaryvertices


Neural Network AnalysisNeural Network Analysis

• After final selection • Neural network used to maximize sensitivity– pT of leptons– m

– MET– angles between MET

and leptons– minimum transverse

mass

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Search for H→WW →Search for H→WW →++--

• Important channel for mH>140 GeV

• Final state:2 leptons + MET– Explicit mass cannot be

reconstructed– Signal / background

separation by exploiting event topology differences

WW decays from a spin 0 particle leptons prefer to decay in the same direction


Combined SM Higgs Combined SM Higgs ResultsResults

• Use as inputs the discriminant outputs from each analysis


Kinematic Distributions of Kinematic Distributions of b-jets b-jets


b-tagging in ZH→b-tagging in ZH→++--bbbb

• Use both single and double-tagged events– S/B are different

• Single tag b=45%, j=0.5%

Electron Channel

Muon Channel

Observed 73 87

Expected 57 21 101 38

Higgs (mH =115GeV)

0.23 0.31


b-tagging in ZH→b-tagging in ZH→++--bbbb

• Double tag b=72%, j=6% per jet

Electron Channel

Muon Channel

Observed 24 53

Expected 28 8 46 13

Z + jets (udsgc)

10 19

Z + bb 12.0 18.5

Tt-bar 3.1 5.3

Higgs (mH =115GeV)

0.23 0.30


Neural Network AnalysisNeural Network Analysis

• 9 kinematic variables used• 10k signal events and 100k background events• NN architecture optimized to yield best

significance


MET + b-jetsMET + b-jets

• Asymmetric b-tagging requirements on the two hadronic jets– maximizes sensitivity

40340 events 439 events


Standard Model Higgs Standard Model Higgs DecaysDecays

• Higgs prefers to decay to massive particle kinematically allowed

• bb for MH < 140 GeV

• WW for MH> 140 GeV


ZH→ZH→++--bb Selectionbb Selection

• Data: 1.1 fb-1

• Dileptons– pT>15 (10) GeV for e ()

– In well-instrumented region of the detector and isolated

– 70 GeV < M < 110 GeV

• Jets– pT>15 GeV and ||<2.5

– b-tagging

• After dilepton+jets selection before b-tag

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Standard Model Higgs Searches at D Ø

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