# Presentation: 658671

## 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

- 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    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

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

**SM Higgs Production **

**SM Higgs Decay  **

- For MH < 135 GeV/c2  use H  bb
-  For MH >135 GeV/c2  use the rising H WW*

## 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:

## WH  lbb                       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

-  (7,9,1)
- Network

## 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

## Di-Jet Mass Resolution

- Used
- m/m = 10%

## Jet Energy Corrections

**Jet Energy Corrections**

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

- Use NN to learn
- The correction
- Function
-              

## 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%

## 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 

- 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**

## WH(100 GeV/c2) vs. Wbb

**WH(100 GeV/c****2****) *****vs*****. Wbb**

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

## WH(100 GeV/c2) vs. WZVariables

**WH(100 GeV/c****2****) *****vs*****. WZ****Variables**

## WH(100 GeV/c2) NN Distributions

**WH(100 GeV/c****2****) NN Distributions**

## Optimizing Cuts (WH100)

**Optimizing Cuts**** (WH100)**

## WH(110 GeV/c2) NN Distributions

**WH(110 GeV/c****2****) NN Distributions**

## Combined Network

**    ****NN1**
**WH-Wbb**

**    ****NN2**
**WH-WZ **

**    ****NN3**
**WH-top**

**    ****NN4**

## Combined Network (WH110)

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

## Results, Standard vs. NN

- Comparison of           achievable with conventional and neural network cuts..

## Standard vs. NN

**Standard *****vs*****. NN**

## High Mass (MH >130 GeV/c2 )

**Consider inclusive production**
**Leading contribution comes from the gluon fusion channel**
**     **
****** ****(gg********H) x B(H ********WW*)x **
**    ****B(W********e/********)**
**    ****-------------------------------------------------------**
**    ****M****H**** (GeV) 140   150   160   170   180   190**
**    ******** ****x B(fb)    11.2  12.8  13.9  12.2   9.8    6.9**
**	****	****     **
**    ****Background ~ 25 pb;  S/B = 4 x 10****-4**
**After all cuts, for 170 GeV: S~0.5 in 30 fb****-1**

**T. Han, A.S. Turcot and R. Zhang, Phys. Rev. D 59, 093001 (1999)**

**SM Higgs Summary**

## 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

## 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!