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Prospects for SM Higgs at the Tevatron

Pushpa BhatFermilab

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• Collaborators:– R. Gilmartin– H. Prosper– Run II SUSY/Higgs Working Group

P.B., R.G., H.P., PRD 62, 074022 (2000)

Run II Higgs Working Group Report, hep-ph/0010338

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Outline

• Low mass Higgs MH < 130 GeV/c2

– WH, ZH Production• Experimental Issues

– b-tagging– Mass resolution– Signal-background discrimination– Final Analysis

• High Mass Higgs MH > 130 GeV/c2

• Other prospects• Conclusions

bb

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The dominant process is gg H. But, it is swamped by di-jet production. The promising channels are WH and ZH.

The main decay mode for low MH iswhich has a branching ratio of about 85%.

bbH

SM Higgs Production

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SM Higgs Decay

•For MH < 135 GeV/c2 use H bb

• For MH >135 GeV/c2 use the rising H WW*

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Low Mass Higgs Study

P.Bhat, R.Gilmartin, H.Prosper, Physical Review D 62, 074022 (2000) Also, in RunII Higgs WG Report hep-ph/0010338

• Signals:

• Channels:

• Backgrounds:

bbEWHZHpp

bbZHpp

bbEWHpp

T

T

or:Emissing

:leptondi

:lepton single

T

bbbbZHpp

bbWHpp

,

tbtbqttWZZZbZbbWbpp ,,,,,,

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WH lbb Single Lepton Channel

• Signal– WH

• Backgrounds– Wbb– WZ– tqb– tb– ttbar

• Detector Simulation– SHW

• Networks– Jetnet (v3.0)– 3 Networks/MH

• WH vs WZ• WH vs Wbb• WH vs tt

– 3 cuts

lT

T

bb

bT

bT

E

bbRSH

ME

E

),( 21

2

1

(7,9,1)Network

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b-tagging

• Required one tight b-tag (SVX) and one loose b-tag (SVX or SLT)– Mean double b-tag efficiency = 45%

• LEP experiments have impressive b-tagging using Neural Networks

• Use NN to combine lifetime and kinematic variables– Work in progress both at CDF & DØ

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Di-Jet Mass Resolution

Usedm/m = 10%

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Jet Energy Corrections

Model corrected jet energy as A function of transverse energy and eta.

Use NN to learn The correction Function

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Higgs Mass ResolutionUse mjj and HT (= Et

jets ) to train NNs to predict the Higgs

boson mass

13.8% 12.2%

13.1% 11..3%

13%13% 11%11%

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• Instead of conventional reconstruction of jets and use of one measured energy per parton, use maximal use of jet energy shape/flow.– Ideas by Fyodor Tkatchov, later versions by

Walter Giele– Currently studies in the JEF project (Px)

and by Bhat, Giele, Prosper• The di-jet invariant m(j,j) is

Probabilistic Jets for Improved Mass Resolution

))2,2(),1,1((2211)),(( JJmddddjjmP

)'()'(')( mmAmEdmmJ

1)( mdmA

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WH(100 GeV/c2) vs. Wbb

• 3 Neural Networks to suppress Wbb, WZ, Top • 7 Variables used

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WH(100 GeV/c2) vs. WZVariables

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WH(100 GeV/c2) NN Distributions

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Optimizing Cuts (WH100)

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WH(110 GeV/c2) NN Distributions

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Combined Network

NN1WH-Wbb

NN2WH-WZ

NN3WH-top

NN4

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Combined Network (WH110)

No improvement in significance by fitting the NN distributions for single lepton channel

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Results, Standard vs. NNchannel mass standard neural LNN/Lstd

(GeV) cuts net (for 5s obsv.)100 0.98 1.44 0.46110 0.69 1.11 0.39120 0.58 0.87 0.44130 0.44 0.55 0.64100 1.09 1.38 0.62110 0.85 1.20 0.50120 0.67 1.00 0.49130 0.54 0.78 0.47100 0.48 0.63 0.58110 0.40 0.52 0.59120 0.40 0.48 0.69130 0.33 0.42 0.61

bbT

bbT

bb

Comparison of achievable with conventional and neural network cuts..

BS/

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Standard vs. NN

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SM Higgs Summary

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Work in Progress

• Systematic selection of variables• Use of large Monte-Carlo samples• Continue Study of b-tagging, mass

resolution• Fitting distributions instead of

counting above cuts using new results in all channels

• Add W*W* contributions at MH 130 GeV/c2

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Conclusions

• Strong theoretical motivations and some experimental hints suggest that Higgs maybe around the corner.

• With 15-20 fb-1 per detector, the prospects for discovery in the low mass region are pretty good!

• We will need all the sophistication possible to make optimal use of data to improve b-tagging, di-jet mass resolution and signal-background separation.

• Finding Higgs at the Tevatron seems extremely challenging. But, who knows? We could have surprises in store! Maybe an easy discovery with diffractive process or many clever improvements!