400 likes | 538 Views
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.
E N D
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
Integrated Luminosities for Higgs Discovery at 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
(mH<130 GeV) (mH>130 GeV) SM Higgs final states use b-tagging to reduce background use leptons to reduce QCD backg. M.Spira use particular lepton signatures, use angular correlations to reduce di-boson backg.
Typical cross-sections ( TeV) s[pb] (mH=100 GeV) gg H 1.0 WH 0.3 ZH 0.18 WZ 3.2 WZ/ZH production are preferred Wbb 11 tt 7.5 tb+tq+tbq 3.4 QCD O(106)
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;
PRD62,2000 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
NN for Higgs search: training variables WH -> ev bb Dark – Signal Light - background
NN for Higgs search : NN Output WH Signal - D=1 WBB Bkgd – D=0
NN and Higgs Search : required Luminosities Compared required Luminosities for Higgs Discovery NN cuts and Standard Cuts
NN and Higgs Search : Luminosity further studies YES 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 NO
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) bottom charm primary R. Demina, march 2000 Points- single m data, black - fit.
Channel-Independent B Tag NN output (m jet) bottom charm primary 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 ?)
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 contributions
Mass Resolution– Parton and Particle jets- Final State Radiation contributions
Mass Resolution– Detector jets - Final State Radiation contributions Signal W H (M_H = 100 gev ) Background W b b
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 Use mjj and HT (= Etjets ) to train NNs to predict the Higgs boson mass 13.8% 12.2% 13.1% 11..3% 13% 11%
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