# Presentation: 173888

## NEURAL NETWORKS IN HIGGS PHYSICS

- Silvia Tentindo-Repond,
- Pushpalatha Bhat and Harrison Prosper
- Florida State University and Fermilab – D0
- ACAT - Fermilab 16 Oct 2000

## Higgs Physics

- The most challenging task of HEP ( Tev and LHC ) in the coming decade will be the search for Higgs.
- In many theories, the Higgs Boson would explain the still mysterious fundamental mechanism of the *electro-weak symmetry breaking  *(EWSB).
- SM predicts Higgs in the mass range                                    107 Gev ( - 45, + 67 )
- MSSM  predicts a lighter Higgs at 130Gev, that would be reachable at Tev
- Studied here : 90  < Mhiggs < 130 Gev

- S.Tentindo ACAT Oct 2000

## Predicted Higgs Mass from SM (measured Mtop and MW)

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- Integrated Luminosities for Higgs Discovery
- at Tev vs Higgs Mass (SM Higgs)

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## Multivariate Methods vs Traditional in Higgs Physics

- Multivariate Methods (NN)  are used to maximize the chance to discover the Higg Boson
- To reduce the required Luminosity for equal signal Significance (S/sqrtB)
- To reduce the required Luminosity for making a 5 sigma discovery

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## SM Higgs final states

**use b-tagging to reduce background**

**use leptons to reduce QCD backg.**

**use particular lepton signatures,**
**use angular correlations to reduce**
**di-boson backg.**

**M.Spira**

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## Typical cross-sections (       TeV)

** ********[pb] ****(m****H****=100 GeV)**

**Typical cross-sections (       TeV)**

**gg    H**

**WH**

**ZH**

**1.0**

**0.3**

**0.18**

**WZ**

**Wbb**

**3.2**

**11**

**tt**

**tb+tq+tbq**

**7.5**

**3.4**

**QCD**

**O(10****6****)**

**WZ/ZH production are preferred**

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## Traditional Analysis vs NN

- Example :                                                                  p p  W H  l v b b       signal                                 p p  W b b                    background
- Need to enhance signal over background                      Use global event variables (Ht, Sph, Apla,MissEt, etc)          + jet variables ( Etj, Etaqj,Ehad, Eem,Ntr,Etr,btag,Ht                    InvMass(jj), etc )
- Use corrections ( e.g. jet energy corrections). Use parametrized b  tag – displaced vertex,soft lepton - etc.)

- S.Tentindo ACAT Oct 2000

## Traditional analysis vs NN (cont.)

- Traditional Analysis improves S/B by imposing  cuts  to each event variable. Rarely optimized,unless signal and background distributions are well separated.
- Multivariate Analysis uses for example NN to find optimal cuts. optimizes separation between signal and background; therefore  maximizes the chance of discovery;

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## Example of NN for Higgs Search

-    Study the process
-     p p -> W H -> l v b b       signal                                                p p -> Z H  -> l+ l- b b                                                             p p -> Z H  -> v v b b
- NN analysis of these three processes leads to remarkable Luminosity reduction allowing Higgs  ( 90 < MH < 130) discovery at Tev
- NN variables used to train :
-     Etb1, Etb2, M(bb), Ht,Ete,ETAe,Etmiss, S, dR(b1,b2), dR(b1,e)
- NN configuration : 7 input – 9 hidden nodes – 1 output node

*PRD62,2000 *

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## NN for Higgs search: training variables 

*WH -> ev bb*

*Dark – Signal *
*Light - background*

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## NN for Higgs search : NN Output

*WH  Signal  -  ****D=1***

*WBB Bkgd – ****D=0***

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## NN and Higgs Search : required Luminosities

*Compared required*
*Luminosities for *
*Higgs Discovery *
*NN  cuts and *
*Standard Cuts*

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## NN and Higgs Search :  Luminosity further studies

- Can we do better ??

- Re-train NN :
- Configuration 6-6-1
- same as previous, but no S
- Different number of epochs
- and hidden nodes .......
- ---------------------------------
- Configuration 8-6-1
- - same as before, add ntrj1 and ntrj2

- YES

- NO

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## NN and Higgs Search :  Luminosity further studies

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## NN and Higgs Search :  Luminosity further studies

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## Channel-Independent  B tagging with NN for Higgs Search 

*“**Heavy Flavor Tagging “ *( C and B jet tagging) [ R. Demina ]
-  Traditional Analysis makes  no distinction  from b and c.
-    NN Analysis  combines
- _ __lifetime variables  _ (track consistent with secondary vertex,
-  Impact Parameter ) and
- _kinematic variables _(mass, fragmentation)
- This tagging method can potentially outperform existing
- Tagging algorithms .

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## Channel-independent B Tagging    NN output (bottomness)

**bottom**
**charm**
**primary**

**Points- single ******** data, black - fit.**

- R. Demina, march 2000

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## Channel-Independent  B Tag         NN output ( jet) 

**primary**

**bottom**

**charm**

- R. Demina – march 2000

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## Channel-dependent  B tagging with NN for Background  Reduction in Higgs Search

- In this study:
- Signal          1000  W H   e v b b                                       Background  1000  W bb                                           --------------------------------------
-    Parton level Monte Carlo: PYTHIA
-                                       ( later on  CompHEP )                                  Parton fragmentation  : PYTHIA                                       Approximate response of Detector ( D0/CDF) : SHW program  - includes simulation of trigger, tracking, cal cluster, reconstruction and b tagging . [J.Conway]

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## Channel-dependent B tagging with NN (cont.)

-  Cuts for base sample:  Pte > 15 Gev/c                                    ETAe < 2, Met > 20 Gev, Etjet >10Gev, Njet>=2, ETAjet<2
- Select jet variables that are connected to b tag of jet
-    Selected: Etjet, Ntr jet, Width jet
- Train NN with a *signal *sample: WH  e v b b
- NN configuration : 3 - 5 – 1                                                          3  input nodes  Etjet, Ntr jet, Width jet                                    5 hidden nodes                                                                     1  output          Channel-Dependent “ B tag “
-  Set NN function ( D= 1 for B jet, D=0 for non B jet)

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## Channel-dependent B tagging with NN (cont.)

**QUESTION :**  Does this channel-dependent b-tagging push to *lower values* the background            ( **Wbb **Massjj distribution ?)

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## Channel-dependent B tagging: Jet variables for NN training

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## Channel-dependent B tagging :Jet variables for NN training

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## NN HB Tag output for B-flavor/no-B-flavor jets ( j1)

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## NN HB Tag output for B-flavor/no-B-flavor jets ( j2)

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## NN HB Tag output for Wbb

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## NN HB Tag output for WH (100)

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## NN HB Tag cut=0.4 WH(100)

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## Channel-dependent B tagging :separation signal/background

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## Improving Mass Resolution with NN in Higgs Search

- M(jj) has proven to be a critical variable to discriminate signal from background in Higgs physics, for *any *channel analysis
- The assumed mass resolution in the recent RunII Susy/Higgs Workshop is 10%.
- Methods and algorithms have still to be worked out to reach such resolution

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## Mass Resolution – Parton and Particle jets- Final State Radiation contributions

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## Mass Resolution – Parton and Particle jets- Final State Radiation contributions

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## Mass Resolution – Detector jets - Final State Radiation contributions

*Signal  ****W H ***
***(M_H = 100 gev ) ***

*Background ****W b b***

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## Improve Mass resolution with NN in Higgs Search ( cont.)

- Possible strategies:
- Study correlations of jet properties and Inv Mass distribution.
- Make a correction function to improve Pt and Energy Resolution of jets and recalculate  Inv. Mass of jets  with the corrected values of Pt and E

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## Improve Mass resolution with NN in Higgs Search ( cont.)

- Study *correlations *among Jet variables and Massjj         Jet Variables :  Nj,  Et, Phi, ETA, d(e,j),               Eem,  Ehad, Etr, Ntr, Wid,
-  plus :  Btag, d(b,j) , d(j,j),    Mjj , Mbjj .
- No clear evidence of correlation.
- Apply *corrections *to Pt and E that could improve the  Mjj resolution.

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## Corrections to Mass Resolution I

- Train NN to correct Mjj  by giving Mjj and Ht                         and forcing the output to be the *true * Higgs mass, for several values of Higgs masses
- NN configuration : 2-6-1                                                  2 input nodes  ( Mjj, Ht )
-     6 hidden nodes,                                                     1 output node ( MH) for several MH *                                    300 epochs                                                       500 examples for each Higgs Mass
***  MH = 100, 105,110,115,120,125,130,135,140**

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## Improving the Higgs Mass Resolution

-  13.8%

- 12.2%

- 13.1%

- 11..3%

- 13%

- 11%

- Use mjj and HT (= Etjets ) to train NNs to predict the Higgs boson mass

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## Corrections to Mass Resolution II

- Train NN to correct Pt and E of jet, by giving Pt distributions at parton level.                                         Generate a corrected Pt function  Ptc(Et, Eta) to apply to Mjj .
- NN configuration : 2-9-1                                                  2 input nodes , 9 hidden nodes, 1 output node ( Mjj )   5000 examples
*    .................................**..*

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## Summary

- NN used to maximize *Discovery **Potential*
*B Tagging** and good **Mass ( Mjj) Resolution** *
- NN for B Tagging is very promising                  ( could *Channel-Dependent  *B Tagging be used for reduction of Background ? )
- Plan to continue systematic studies of the methods

- S.Tentindo ACAT Oct 2000