NEURAL NETWORKS IN HIGGS PHYSICS

1. NEURAL NETWORKS IN HIGGS PHYSICS Silvia Tentindo-Repond, Pushpalatha Bhat and Harrison Prosper Florida State University and Fermilab – D0 ACAT - Fermilab 16 Oct 2000

2. 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

3. Predicted Higgs Mass from SM (measured Mtop and MW)

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

5. 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

6. (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.

7. 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)

8. 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.)

9. 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;

10. 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

11. NN for Higgs search: training variables WH -> ev bb Dark – Signal Light - background

12. NN for Higgs search : NN Output WH Signal - D=1 WBB Bkgd – D=0

13. NN and Higgs Search : required Luminosities Compared required Luminosities for Higgs Discovery NN cuts and Standard Cuts

14. 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

15. NN and Higgs Search : Luminosity further studies

16. NN and Higgs Search : Luminosity further studies

17. 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 .

18. Channel-independent B Tagging NN output (bottomness) bottom charm primary R. Demina, march 2000 Points- single m data, black - fit.

19. Channel-Independent B Tag NN output (m jet) bottom charm primary R. Demina – march 2000

20. 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]

21. 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)

22. Channel-dependent B tagging with NN (cont.) • QUESTION : Does this channel-dependent b-tagging push to lower values the background ( Wbb Massjj distribution ?)

23. Channel-dependent B tagging: Jet variables for NN training

24. Channel-dependent B tagging :Jet variables for NN training

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

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

27. NN HB Tag output for Wbb

28. NN HB Tag output for WH (100)

29. NN HB Tag cut=0.4 WH(100)

30. Channel-dependent B tagging :separation signal/background

31. 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

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

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

34. Mass Resolution– Detector jets - Final State Radiation contributions Signal W H (M_H = 100 gev ) Background W b b

35. 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

36. 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.

37. 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

38. 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%

39. 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 ……………………………..

40. 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