1 / 44

Recent Results in Susy Higgs Searches at DØ

Recent Results in Susy Higgs Searches at DØ. Fermilab Joint Experimental-Theoretical Seminar Friday, 12 th November 2010. Jonathan Hays On behalf of the DØ Collaboration. Outline. Introduction Searches for Higgs + b-jets tau final states (b ) b-jet final states ( bbb )

corine
Download Presentation

Recent Results in Susy Higgs Searches at DØ

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Recent Results in Susy Higgs Searches at DØ Fermilab Joint Experimental-Theoretical Seminar Friday, 12thNovember 2010 Jonathan Hays On behalf of the DØ Collaboration

  2. Outline • Introduction • Searches for Higgs + b-jets • tau final states (b) • b-jet final states (bbb) • Conclusions and Outlook Wine and Cheese Seminar

  3. Tevatron Tevatron and the detectors continue to perform very well 5.2 fb-1 4.3 fb-1 ~16 fb-1 expected by Oct 2014 Over 9.6 fb-1 delivered Wine and Cheese Seminar

  4. D-Zero Wine and Cheese Seminar

  5. Standard Model • Highly successful theory but: • No dark matter candidate • No gravity • Hierarchy and naturalness problems • No unification Wine and Cheese Seminar

  6. Supersymmetry Introduce new space-time symmetry between fermions and bosons • Solves naturalness problem • LSP = dark matter candidate ? • Supergravity • GUT unification possible hep-ph/9709356 Wine and Cheese Seminar

  7. tan()=30 Total A H h MSSM MSSM Higgs Sector • 2 Higgs doublets • 5 physical scalars: • 3 neutral: h, H, A • 2 charged: H± tree level two parameters: mA and tanβ σMSSM ~ 2×Br×tan2β×σSM Chance of discovery before SM sensitivity! A. Djouadi, hep-ph:0810-2439 Radiative corrections large brings in dependence on other model parameters Wine and Cheese Seminar

  8.  → tt   bf → 3b/btt   bbf→ 4b/bbtt MSSM Higgs Enhancement to “down-type” fermions φ = (h,H or A) BR(φ→bb) ~ 90% BR(φ→) ~ 10% φ→ clean signatures but low BR bφ→b reduced backgrounds added sensitivity at low mA bφ→bbb large background high BR Wine and Cheese Seminar

  9.  → tt   bf → 3b/btt   bbf→ 4b/bbtt MSSM Higgs Enhancement to “down-type” fermions BR(φ→bb) ~ 90% BR(φ→) ~ 10% φ→ clean signatures but low BR bφ→b reduced backgrounds added sensitivity at low mA bφ→bbb large background high BR Wine and Cheese Seminar

  10.  → tt Inclusive Searches φ→ φ→ Wine and Cheese Seminar

  11. Inclusive Searches Tevatron combination φ→ http://arxiv.org/abs/1003.3363v3 Wine and Cheese Seminar

  12. Exclusive Searches Published results from DØ bφ→b bφ→bbb Phys. Rev. Lett. 104, 151801 (2010) Phys. Rev. Lett. 101, 221802 (2008) Wine and Cheese Seminar

  13. Search Strategy • Optimise analysis based on expected limits with full systematics • In absence of significant discrepancy between data and background: • Set limits in (almost) model independent way • Set limits in benchmark SUSY scenarios • Combine results across channels for particular model choices Wine and Cheese Seminar

  14.  Signal Modelling Use 5 flavour number scheme: Generate gb→bh at LO in PYTHIA Acceptance cuts on the spectator b-jet Reweighted in pt and eta of spectator b-jet based on MCFM calculation Important differences in kinematics when moving from LO to NLO Wine and Cheese Seminar

  15. Signal Modelling • Large enhancements to the couplings give large widths Radiative corrections have significant effect Larger effect for bbb channels Less significant for bττ Simulate widths using “narrow” samples and convoluting with Breit-Wigner Wine and Cheese Seminar

  16. b-jet identification Several mature algorithms used: 3 main categories: - Soft-lepton tagging - Impact Parameter based - Secondary Vertex reconstruction Wine and Cheese Seminar

  17. b-jet identification Measure b & c efficiencies on b-jet enriched sample Fake rate measured on multijet sample Composition estimated from secondary vertex mass templates Data MC NIM A 620, 490 (2010) MC and data differences Reweight with TRFs Direct tagging Tag rate functions (TRF) parameterise efficiencies and fake rates versus pt and eta b-tagged samples Wine and Cheese Seminar

  18.  • 1 TRK  wide CAL cluster Type 1 Type 3     no TRK, but EM sub-cluster o   Type 2     TRK  CAL TRK  CAL    -lepton identification Leptonic decays – single isolated leptons Neural network (NN) trained for each type to discriminate against jets Hadronic decays categorised by decay mode Efficiencies measured in clean Z sample Wine and Cheese Seminar

  19. Searches in tau final states bφ→bτµτhad • 4.3 fb-1 integrated luminosity • Collected with single muon trigger • Dominant backgrounds: • Z→ + jets • top pairs • multi-jet (QCD + W+jets) • Event selection: • Single isolated muon • Opposite sign had • 1 loose b-tagged jet ( ε ~ 71%) Complementary to φ→ and bφ→bbb Preselection No b-tag Wine and Cheese Seminar

  20. Searches in tau final states • Train NNs to discriminate against top and multi-jet backgrounds NN b-tagger suppresses Z+jets background Combine all NNs into single discriminant Final discriminant = geometric mean of 3 NN outputs Wine and Cheese Seminar

  21. bφ→bμhadlimits 4.3 fb-1 preliminary results Tree level limit Most stringent limit at low MA Limits set using “CLs” method Wine and Cheese Seminar

  22. Searches with b-quarks New result with 5.2fb-1 data • 5x more data • Extended mass range: 90-300 GeV • Larger MC samples Submitted to Phys. Lett. B arxiv.org:1011.1931 Major improvements since previous 1fb-1 publication Expanded and improved treatment of systematics - e.g. b-tagging Re-analyzed old 1fb-1 data set Wine and Cheese Seminar

  23.  bf → 3b/btt Searches with b-quarks 5.2fb-1 collected with jet triggers – making use of lifetime information 3 or 4 jets, 3 must be b-tagged Kinematic likelihood (D) used to select best jet pairing, + cut to suppress background Very large multi-jet background Challenging to model → data driven method Multijet cross sections not well predicted → float normalisation Wine and Cheese Seminar

  24. 3 b-tag background MC correction factor 2 b-tag data Background Modelling 2D correction: likelihood vs invariant mass Add plot here... Predict background shape from 2-tagged data with correction from MC Wine and Cheese Seminar

  25. Background Modelling:Sample composition Needed for MC correction factor Estimated using MC fit to data over several b-tag operating points In 3-tag sample bbb ~ 47% bbj ~ 32% bbc ~ 17% ccj ~ 2% Wine and Cheese Seminar

  26. Background Modelling • Validate modelling in a signal poor region • “wrong” jet pair looks like background • Pick lowest likelihood pairing • and select D < 0.12 Excellent agreement seen between model and data Wine and Cheese Seminar

  27. Kinematic Likelihood Trained on jet-pairings Two likelihoods: low mass MA < 140 GeV high mass MA ≥ 140 GeV In each event select pairing with highest LH value Cut on LH optimised considering expected limits with full systematics LH > 0.65 for all mass points Wine and Cheese Seminar

  28. Kinematic Likelihood Projection of 2D distributions onto likelihood axis Cut Cut Wine and Cheese Seminar

  29. Mass distributions Di-jet invariant mass distribution used as input for the limit setting D > 0.65, background normalised to data Wine and Cheese Seminar

  30. Systematics Signal: dominated by b-tagging (15%-20%) and jet energy scale (2-14%) includes both rate and shape systematics Only consider variations in shape Background : normalisation included as nuisance parameter Wine and Cheese Seminar

  31. Systematics: Fake-rate An area of major improvement since 1fb-1 result remeasured on hbb specific samples Detailed approach to systematics Fake rate b-tagging SF SVT Template fit Fake rate Fake rate determination Sample composition Wine and Cheese Seminar

  32. Results Wine and Cheese Seminar

  33. Results Small excess ~ 2.5σ After trials factor ~ 2.0 σ Wine and Cheese Seminar

  34. SUSY Benchmark Scenarios • Five additional parameters due to radiative correction • MSUSY (parameterizes squark, gaugino masses) • Xt (related to the trilinear coupling At→ stop mixing) • M2 (gaugino mass term) •  (Higgs mass parameter) • Mgluino(comes in via loops) • Two common benchmarks • Max-mixing - Higgs boson mass mh close to max possible value for a given tan • No-mixing - vanishing mixing in stop sector → small mass for h Wine and Cheese Seminar

  35. MSSM Scenario Limits μ>0 suppressed production x BR – only set limits for μ<0 Wine and Cheese Seminar

  36. Outlook Still large potential for improvements: More data: 5 → 7+ fb-1 Improved b-tagging → 30% (bbb) yield Improved analysis techniques e.g. Event based discriminants→ 15-30% sensitivity Wine and Cheese Seminar

  37. Outlook: Combinations (φ→) +(bφ→b) + (bφ→bbb) Combine within channels – D0 + CDF – can be done in roughly model independent way Combine across channels – generally requires picking a model Aim for new D0 combination by Moriond with up to ~7fb-1 Preparations for Tevatron combination also underway φ→ Wine and Cheese Seminar

  38. Outlook: SM Contributions? • eg P. Draper et al. • arXiv:0905.4721v2 Interpret SM limits within MSSM Real potential to probe large region of MSSM Higgs parameter space Wine and Cheese Seminar

  39. Conclusions • Interesting time to be doing Higgs searches at the Tevatron! Large data sets + sensitive analyses = discovery potential! Wine and Cheese Seminar

  40. Backup slides Wine and Cheese Seminar

  41. Mass distribution Background normalised to data-signal (S+B = D) 3-jet channels Wine and Cheese Seminar

  42. ? Wine and Cheese Seminar

  43. Limit Setting • Use modified frequentist method “CLs” • Test statistic: negative poisson log likelihood ratio • Pseudo-experiments to extract likelihood distribution for B and S+B hypotheses Systematics incorporated as Gaussian smearing in pseudo-experiments Wine and Cheese Seminar

  44. LLR Distributions CLb CLs+b Signal like Background like Wine and Cheese Seminar

More Related