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Search for associated Higgs Production in the Tau Channel

Search for associated Higgs Production in the Tau Channel. October 21 2008 Yury Pogorelov University of Notre Dame. Overview. Standard model Motivation for the Higgs search MSSM higss production at the Tevatron Data and MC samples Analysis Conclusion.

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Search for associated Higgs Production in the Tau Channel

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  1. Search for associated HiggsProduction in the Tau Channel October 21 2008 Yury Pogorelov University of Notre Dame YuryPogorelov

  2. Overview Standard model Motivation for the Higgs search MSSM higss production at the Tevatron Data and MC samples Analysis Conclusion Yury Pogorelov

  3. Standard Model of particle physics Three generation of Matter (fermions) Interaction mediators (bosons) • All the observed phenomena (except gravity) are descried by the Standard Model: • Weak force is decay responsible for ¯ -decay • Strong force holds neutron and protons inside nuclei • Electromagnetic is responsible for chemistry YuryPogorelov

  4. Higgs Field and masses in the SM • Particle masses in the SM are the result of interaction with doublet of Higgs scalar fields H=(H0, H-) • Non Zero vacuum expectation value (VEV) of the Higgs field results in the breaking of electroweak symmetry and W/Z having masses. • Fermions acquire mass through the Yukawa coupling • Higgs field coupling to the usual particles like fermions and W/Z bosons means that Higgs boson can be produced in the interactions of ordinary particles Yury Pogorelov

  5. Motivation for the new physics • Despite its success, SM have • some unresolved problems: • Large number of free parameters • Hierarchy problem. Radiative corrections to the • Higgs mass are quadratic and predict large mass despite the fact that VEV of the Higgs field is small. • Strong and electroweak forces are not unified Yury Pogorelov

  6. SuperSymmetry SuperSymmetry (symmetry between bosons and fermions) offers possible solution of the SM problems: Additional degree of freedoms cancel radiative corrections. However, no superparticles have been observed. Which means that that SuperSymmetry is broken and superparticles are much heavier. Yury Pogorelov

  7. Higgs in MSSM • Minimal Supersymmetric extension of the SM contains two Higgs doublets. After electro-weak symmetry breaking five Higgs bosons remains: • CP-even, h and H, • CP-odd, A • Two charged states, H+ and H- • Higgs mA and ratio between two vacuum expectations tan¯ control the Higgs sector of the MSSM Yury Pogorelov

  8. MSSM Higgs production on the Tevatron Br( H!¿¿) = ~10% Yury Pogorelov

  9. Tevatron 4 miles in circumference Superconductive accelerator with 1.96 TeV C.M. Energy. Typical Luminosity 30e30 Two detectors Yury Pogorelov

  10. Dzero Detector • Liquid argon calorimeter • 2 Tesla Superconducting solenoid • Central Fiber tracker • Silicon Microstrip Tracker • Muon system. Yury Pogorelov

  11. Data and Monte Carlo samples Data • 400 pb-1 Run II data recorded between August 2002 and July 2004 • 1MUloose skim (at least one loose muon with pT>12GeV) • 277808 events after requiring ¿-candidate, muon with pT>12GeV , and selected muon triggers. • 328 pb-1 of data after removing runs declared as having bad quality for Muon, Calorimeter, CFT/SMT • Signal MC is Standard Model PYTHIA process ppbar ! bH! b¿+¿- • with one tau forced to decay to muon. • ttbar,w+2j,WW :Alpgen+PYTHIA • Cross-Sections are from MCFM+FeynHiggs YuryPogorelov

  12. Current searches and basic idea for bh! b¹¿ search • bh!bbb D0, CDF • h! ¿ ¿ (e¿ , ¹ ¿) D0, CDF • bh! b¹¿ D0 • Look for ¹¿ pairs with associated low pt jet(s). • Measure btag-rate and compare to that of ¹ ¹ channel (Z) • Higgs presence will manifest itself in higher btag-rate in ¹¿ channel • Advantages/disadvantages • Low QCD background • Low statistics (branching into ¹¿ ~3%) • Require four different ID (¹ , ¿ , jet, b-id) • Does not suffer from large radiative corrections (compared to bb channel) to the higgs decay widths (scenario with ¹ >0) • Complimentary to the bb channel YuryPogorelov

  13. -id • Tau type depends on Hadronic decay mode: • -+ (type 1, -like) • -+N0+ (type 2, -like) • -+N(++-)+M0+ (type 3, a-like, 3-prong) • Neural Net for -identification (input variables) • EM12 isolation (Transverse energies of 1,2 EM layers / ET) • Track isolation (pT extra tracks/ pT  tracks) • ET over total ET ( Et / ( ET + pT  tracks) ) •  alpha (angular distance between EM clusters and  tracks) • Profile (ET of 2 highest calorimeter towers / ET ) • EM ET over ET (EM subclusters ET / ET ) • leading track pT / ET • Calorimeter isolation ( (ET(cone0.5)-ET(cone0.3)/ET(cone0.3) ) • RMS ( cluster angular width) Yury Pogorelov

  14. Tau NN distribution for different tau types (MC) Tau Neural Net reduces problem of Tau quality selection to placing a cut on single variable - NN output. Yury Pogorelov

  15. Triggers MU_W_L2M5_TRK10 • L1: muon trigger in the “wide” CFT region (|´|<1.5) with “tight” scintillator • L2: “medium” muon with pT>5 GeV • L3: Level 3 central track with pT> 10Gev MUW_W_L2M3_TRK10 • L1: muon trigger in the “wide” CFT region (|´|<1.5) with “tight” scintillator asnd loose wire • L2: “medium” muon with pT>3 GeV • L3: Level 3 central track with pT>10 Gev Yury Pogorelov

  16. Tau: Tau ET>14GeV, ¿ -rms<0.25 pT(¿-tracks) >7 GeV for type 1 and 3, greater than 5 GeV for type 2. Type 1 and 2 TauNN > 0.8, Type 3 TauNN>0.98 ¢Á (¹,¿) > 2.0 and tau does not math to any muon in the event Muon (from tau decay): Loose quality muon with PT>12 matched to the central track Cal. Isolation: E in cone of R=0.1 < 4 GeV Cal. Isolation: E(R=0.4)-E(R=0.1) < 4 GeV Track isolation: pT(trks)<2.5 in the R=0.5 cone Event selection Yury Pogorelov

  17. Event selection (continued) Jet • Jet with ET>15 and detector || <2.5 • 0.05<EM fraction <0.95 • Coarse hadronic fraction (CHF<0.4 and f90<0.5) or (CHF<0.15) • (Hot cell fraction <10) and (n90>1) • Isolated (R=0.5) from selected muon and tau. • Btagging: JLIP with “loose” (mistag rate 1%) cut is used All the objects in the event (muon tau and jet) must be associated with the same primary vertex within ¢Z<1 cm. Yury Pogorelov

  18. b-tagging • Associated higgs production search requires accurate b-identification • B-tagging algorithm is based on the fact that b-hadrons decay weakly after travelling 1-3 mm • Jet Lifetime Impact Parameter tagger (JLIP) uses track’s Impact Parameter to reconstruct b-jets. • To tag a jet, the jet has to meet certain • criteria (taggability): • At least two associated tracks with pT>0.5GeV within jet ¢R<0.5 cone • Tracks have to have at least 3 SMT and 7 CFT hits Yury Pogorelov

  19. Taggability measured in data Taggability is measured in Z+jet!¹+¹- +jet data, which have similar to the Higgs signal Jet kinematics. Both, taggability and TRF are applied to Monte Carlo as weights Yury Pogorelov

  20. Signal kinematic distributions:Tau ET and muonpT Tau ET MH=150…90 GeV MuonpT MH=150…90 GeV Yury Pogorelov

  21. Signal kinematic distributions:tau and muon´ Muon´ MH=150…90 GeV Tau ´ MH=150…90 GeV Yury Pogorelov

  22. Signal kinematic distributions:¢Á(¹,¿) and Invariant Mass M(¹,¿,MET) MH=150…90 GeV ¢Á(¹,¿) MH=150…90 GeV Yury Pogorelov

  23. Signal kinematic distributions:leading jet pT Leading Jet pT MH=150…90 GeV Yury Pogorelov

  24. Main backgrounds and estimation • QCD:Estimated on data: QCD tends to have equal number of OS (opposite sign ) and SS (same sign) events. QCD contribution can be estimated by counting SS events. Asymmetry is measured on QCD enriched sample and corrected for. Exact equations are: • Z+btag/mistag:estimated by counting Z(!¹¿)+jets events in data and applying b-tag rate measured on Z(!¹ ¹)+jets events. • ttbar, W+(bc)jets, WW:Estimated using Monte Carlo. Yury Pogorelov

  25. QCD enriched distributions (antiisolated mt) Yury Pogorelov

  26. “Signal” enriched distribution (isolated mt) Yury Pogorelov

  27. Zt+t- Reference process, Data-MC cross-check Can we predict amount of Z using Monte Carlo ? All backgrounds (except QCD) are estimated from Monte Carlo Discrepancy between data and MC could be attributed to the differences in Tau Energy Scale YuryPogorelov

  28. Zt+t- Reference process, Data-MC cross-checkEffect of the systematic errors Effect of fluctuating of the background prediction by -1¾ Systematic error of 10% will be assigned on tau reconstruction efficiency to account for this discrepancy Yury Pogorelov

  29. Kinematic NN (KNN) ttbar decaying leptonically is a significant physical background, especially in Type 2 . simple Kinematic NN with 4 input variables is used to reject ttbar events KNN variables: • HTB (Sum of jet pT) • Njet (number of jets) • MHT (missing ET constructed from ¹, ¿ and jets) • ¢Á between ¹ and ¿ Yury Pogorelov

  30. Signal & ttbar KNN distributions KNN distribution after NN training using Higgs signal and ttbar Monte Carlo. There is no significant correlation between NN output and the Higgs mass for the signal MC. Yury Pogorelov

  31. Optimization of the KNN cuttype 2 taus Cut on KNN is optimized for the best expected limit using expected limit as a function of the KNN cut curve. KNN>0.4 is selected for the Type 2 channel Yury Pogorelov

  32. Optimization of the KNN cuttype 3 taus No KNN cut is selected for the Type 3 channel Yury Pogorelov

  33. Optimization of the KNN cuttype 1 taus No KNN cut is selected for the Type 1 channel Yury Pogorelov

  34. Optimization of the Tau NN cut type 2 channel Cut on TauNN is optimized for the best expected limit using expected limit as a function of the TauNN cut curve. TauNN>0.8 is selected for the Type 2 channel Yury Pogorelov

  35. Optimization of the Tau NN cut type 3 channel TauNN>0.98 is selected for the Type 3 channel Yury Pogorelov

  36. Optimization of the Tau NN cut type 1 channel TauNN>0.8 is selected for the Type 1 channel Yury Pogorelov

  37. Data versus background predictionfor three tau types Invariant mass constructed using ¹, ¿ and missing ET. Yury Pogorelov

  38. Errors Summary Yury Pogorelov

  39. Results: H+b(mt +b ) cross section Limit Cross-Section limit on ¾(h+b)*Br(h !¿¿) Comparison with p14 bbb results, (Br(h !¿¿) =10% is assumed) Yury Pogorelov

  40. Results: MSSM tanbexcluded MSUSY = 2000 GeV, Xt=0 GeV ¹=§ 200 GeV MSUSY = 1000 GeV, Xt=2000 GeV ¹=§ 200 GeV mg = 800 GeV, M2=200 GeV mg = 1600 GeV, M2=200 GeV Yury Pogorelov

  41. Results: Projected tanblimit Assuming no improvements in the analysis expected limit will be Yury Pogorelov

  42. Conclusion • First ever MSSM higgs search in b+mu+tau channel was conducted • No excess of events over SM backgrounds was observed, therefore limits on Higgs production cross section and MSSM parameter space is set Yury Pogorelov

  43. Trigger efficiency using tag&probe method Trigger efficiency can be measured using Z!¹+¹- events in data: One of the muon from Z decay used as tag and track in the CFT from another muon is used as probe. “Tag” muon: • Medium muon with central track and pT>30GeV • Matched to the L1 and L2 muon triggers • Must not be cosmic • ¢R>2 between tag and probe muons “Probe” muon Is a track with: • pT>20GeV • >7 CFT hits • Â2 /d.o.f <4 • |dca|<0.02 (0.2) for the track with (without) SMT hits “Tag” muon z “probe” muon Yury Pogorelov

  44. L1 scintillator and wire efficiencies Yury Pogorelov

  45. L2 trigger efficencies Yury Pogorelov

  46. L3 tracking efficiencyas a function of track pT L3 tracking efficiency parametrized as function track ´, pT and Z L3 pTdependence for tracks with different Z Yury Pogorelov

  47. L3 tracking efficiencyas a function of track ´ L3´dependence for tracks with different Z All three Levels are combined and assigned to the MC events as trigger weight Trigger efficiency on the Monte Carlo Higgs signal is: 62§1.1 % Yury Pogorelov

  48. b-tagging efficiency in data B-tagging efficiencies are measured in data using three different methods. YuryPogorelov

  49. b-tagging performance in data: mistag rate Probability to tag light jet (mistag rate): • is negative b-tag rate in data • is ratio between the number of negative tagged light jet over the total number of negative jets in QCD MC • is ratio between the number of positive tagged jets from light quark over the number of negative tagged jets from light quarks in QCD MC. YuryPogorelov

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