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

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

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

4. D-Zero 4Wine and Cheese Seminar

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

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

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

8. MSSM Higgs  →  b  → 3b/b    bb  → 4b/bb    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 8Wine and Cheese Seminar φ = (h,H or A)

9. MSSM Higgs  →  b  → 3b/b    bb  → 4b/bb    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 9Wine and Cheese Seminar

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

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

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

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 Seminar13

14. Signal Modelling Wine and Cheese Seminar14 Reweighted in pt and eta of spectator b-jet based on MCFM calculation Important differences in kinematics when moving from LO to NLO Use 5 flavour number scheme:   Generate gb→bh at LO in PYTHIA Acceptance cuts on the spectator b-jet

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

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

17. b-jet identification Wine and Cheese Seminar17 Measure b & c efficiencies on b-jet enriched sample Fake rate measured on multijet sample Composition estimated from secondary vertex mass templates MC and data differences DataMC b-tagged samples Direct tagging Reweight with TRFs Tag rate functions (TRF) parameterise efficiencies and fake rates versus pt and eta NIM A 620, 490 (2010)

18.  -lepton identification Wine and Cheese Seminar18    TRK  CAL Type 1      oo  no TRK, but EM sub-cluster TRK  CAL Type 2       1 TRK  wide CAL cluster Type 3 Hadronic decays categorised by decay mode Leptonic decays – single isolated leptons Neural network (NN) trained for each type to discriminate against jets Efficiencies measured in clean Z sample

19. Searches in tau final states 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%) Wine and Cheese Seminar19 Preselection No b-tag Complementary to φ→  and bφ→bbb bφ→bτ µ τ had

20. Searches in tau final states Train NNs to discriminate against top and multi-jet backgrounds Wine and Cheese Seminar20 Final discriminant = geometric mean of 3 NN outputs NN b-tagger suppresses Z+jets background Combine all NNs into single discriminant

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

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

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

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

25. Background Modelling: Sample composition Wine and Cheese Seminar25 In 3-tag sample bbb ~ 47% bbj ~ 32% bbc ~ 17% ccj ~ 2% Needed for MC correction factor Estimated using MC fit to data over several b-tag operating points

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 26Wine and Cheese Seminar Excellent agreement seen between model and data

27. Kinematic Likelihood 27Wine and Cheese Seminar Trained on jet-pairings Two likelihoods: low mass M A < 140 GeV high mass M A ≥ 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

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

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

30. Systematics Wine and Cheese Seminar30 Background : normalisation included as nuisance parameter Only consider variations in shape Signal: dominated by b-tagging (15%-20%) and jet energy scale (2-14%) includes both rate and shape systematics

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

32. Results 32Wine and Cheese Seminar

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

34. SUSY Benchmark Scenarios 34Wine and Cheese Seminar Five additional parameters due to radiative correction M SUSY (parameterizes squark, gaugino masses) X t (related to the trilinear coupling A t → stop mixing) M 2 (gaugino mass term)  (Higgs mass parameter) M gluino (comes in via loops) Two common benchmarks Max-mixing - Higgs boson mass m h close to max possible value for a given tan  No-mixing - vanishing mixing in stop sector → small mass for h

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

36. Outlook Wine and Cheese Seminar36 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

37. Outlook: Combinations 37Wine and Cheese Seminar 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 →  (φ →  ) + (bφ →b  ) + (bφ → bbb)

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

39. Conclusions Wine and Cheese Seminar39 Interesting time to be doing Higgs searches at the Tevatron! Large data sets + sensitive analyses = discovery potential!

40. Backup slides Wine and Cheese Seminar40

41. Mass distribution Wine and Cheese Seminar41 Background normalised to data-signal (S+B = D) 3-jet channels

42. Wine and Cheese Seminar42

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 Wine and Cheese Seminar43 Systematics incorporated as Gaussian smearing in pseudo-experiments

44. LLR Distributions Wine and Cheese Seminar44 Background likeSignal like CLbCLs+b