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Prospects for SM Higgs at the Tevatron

Prospects for SM Higgs at the Tevatron. Pushpa Bhat Fermilab. 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. Outline. Low mass Higgs M H < 130 GeV/c 2

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Prospects for SM Higgs at the Tevatron

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  1. Prospects for SM Higgs at the Tevatron Pushpa Bhat Fermilab

  2. 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

  3. 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

  4. SM Higgs Production The main decay mode for low MH is which has a branching ratio of about 85%. The dominant process is gg  H. But, it is swamped by di-jet production. The promising channels are WH and ZH.

  5. SM Higgs Decay • For MH < 135 GeV/c2 use H  bb • For MH >135 GeV/c2 use the rising H WW*

  6. 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:

  7. 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 WH  lbb Single Lepton Channel (7,9,1) Network

  8. 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

  9. Di-Jet Mass Resolution Used m/m = 10%

  10. Jet Energy Corrections Model corrected jet energy as A function of transverse energy and eta. Use NN to learn The correction Function 

  11. Higgs Mass Resolution Use mjj and HT (= Etjets ) to train NNs to predict the Higgs boson mass 13.8% 12.2% 13.1% 11..3% 13% 11%

  12. Probabilistic Jets for Improved Mass Resolution • 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

  13. WH(100 GeV/c2) vs. Wbb • 3 Neural Networks to suppress • Wbb, WZ, Top • 7 Variables used

  14. WH(100 GeV/c2) vs. WZVariables

  15. WH(100 GeV/c2) NN Distributions

  16. Optimizing Cuts (WH100)

  17. WH(110 GeV/c2) NN Distributions

  18. Combined Network NN1 WH-Wbb NN2 WH-WZ NN4 NN3 WH-top

  19. Combined Network (WH110) No improvement in significance by fitting the NN distributions for single lepton channel

  20. Results, Standard vs. NN Comparison of achievable with conventional and neural network cuts..

  21. Standard vs. NN

  22. SM Higgs Summary

  23. 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

  24. 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!

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