neural networks in higgs physics

NEURAL NETWORKS IN HIGGS PHYSICS

Silvia Tentindo-Repond,Pushpalatha Bhat and Harrison ProsperFlorida State University and Fermilab –

D0

ACAT - Fermilab 16 Oct 2000

S.Tentindo ACAT 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


Predicted Higgs Mass from SM (measured Mtop and MW)Title:t99_mt_mw_contours.epsCreator:HIGZ Version 1.23/09Preview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.


Integrated Luminosities for Higgs Discoveryat Tev vs Higgs Mass (SM Higgs)


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


SM Higgs final states

use b-tagging to reduce background

use leptons to reduce QCD backg.

use particular lepton signatures,use angular correlations to reducedi-boson backg.

,bbH (mH<130 GeV)

ZZWWH , (mH>130 GeV)M.Spira


[pb] (mH=100 GeV)

Typical cross-sections ( TeV)2s

gg HWH

ZH

1.00.30.18

WZWbb

3.211

tttb+tq+tbq

7.53.4

QCD O(106)

WZ/ZH production are preferred


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.)


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;


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


NN for Higgs search: training variables

WH -> ev bb

Dark – Signal Light - background

Title:var_plots_wbb_v2.epsCreator:HIGZ Version 1.23/07Preview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.


NN for Higgs search : NN Output

WH Signal - D=1

WBB Bkgd – D=0

Title:net_plots_zhnn_100.epsCreator:HIGZ Version 1.23/07Preview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.


NN and Higgs Search : required Luminosities

Compared requiredLuminosities for Higgs Discovery NN cuts and Standard Cuts

Title:res4.epsCreator:HIGZ Version 1.23/07Preview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.


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


NN and Higgs Search : Luminosity further studiesTitle:lum1.epsCreator:HIGZ Version 1.26/04Preview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.


NN and Higgs Search : Luminosity further studiesTitle:lum2.epsCreator:HIGZ Version 1.26/04Preview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.


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 .


Channel-independent B Tagging NN output (bottomness)

bottomcharmprimary

Points- single data, black - fit.

R. Demina, march 2000


Channel-Independent B Tag NN output ( jet)

primary

bottom

charm

R. Demina – march 2000


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]


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)


Channel-dependent B tagging with NN (cont.)

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


Channel-dependent B tagging: Jet variables for NN training

Title:nnbtag1.epsCreator:HIGZ Version 1.26/04Preview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.



Channel-dependent B tagging :Jet variables for NN training




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

Title:j1btag.epsCreator:HIGZ Version 1.26/04Preview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.


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

Title:j2btag.epsCreator:HIGZ Version 1.26/04Preview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.


NN HB Tag output for WbbTitle:btagwbb.epsCreator:HIGZ Version 1.26/04Preview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.


NN HB Tag output for WH (100)Title:btagwh100.epsCreator:HIGZ Version 1.26/04Preview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.


NN HB Tag cut=0.4 WH(100)Title:btag_cut4.epsCreator:HIGZ Version 1.26/04Preview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.


Channel-dependent B tagging :separation signal/background



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


Mass Resolution – Parton and Particle jets- Final State Radiation contributionsTitle:higgs.dviCreator:dvips(k) 5.86 Copyright 1999 Radical Eye SoftwarePreview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.


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

Title:higgs.dviCreator:dvips(k) 5.86 Copyright 1999 Radical Eye SoftwarePreview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.


Mass Resolution – Detector jets - Final State Radiation contributions

Signal W H (M_H = 100 gev )

Background W b b

Title:masssb.epsCreator:HIGZ Version 1.26/04Preview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.


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


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.


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


Improving the Higgs Mass Resolution

13.8% 12.2%

13.1% 11..3%

13%13% 11%11%

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


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

……………………………..


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

neural networks in higgs physics

Documents

higgs boson

higgs physicsthe

lighter higgs

higgs search study

higgs discovery nn cuts

b b p p z h

gevpredicted higgs mass

v b b signal p p z h