1. 1 Sinéad M. Farrington University of Oxford for the CDF Collaboration HQL 15 th October 2010 Rare B and D Decays at CDF

2. 2 Dedicated rare B and D dimuon triggers Will show B→ , B→  h, D→  Using all muon chambers to |  |  1.1 Tracking capability leads to good mass resolution 2 CDF Central Muon Extension (0.6< |  | < 1.0) Central Muon Chambers (|  | < 0.6)

3. 3 3 B d,s   0

4. 4 4 B   in the Standard Model In Standard Model FCNC decay B   heavily suppressed Standard Model predicts A. Buras Phys. Lett. B 566,115 B d   further suppressed by CKM coupling (V td /V ts ) 2 Both are below sensitivity of Tevatron experiments SUSY scenarios (MSSM,RPV,mSUGRA) boost the BR by up to 100x Observe no events  set limits on new physics Observe events  clear evidence for new physics

5. 5 5 The Challenge Large combinatorial background Key elements are select discriminating variables determine efficiencies estimate background search region pp collider: trigger on dimuons   )~24MeV Mass resolution: separate B d, B s

6. 6 Vertex muon pairs in B d /B s mass windows Unbiased optimisation, signal region blind Aim to measure BR or set limit: Reconstruct normalisation mode (B +  J/  K + ) Construct Neural Net to select B signal and suppress dimuon background Measure remaining background Measure the acceptance and efficiency ratios 6 Methodology

7. 7 Proper decay length ( ) Pointing (  ) |  B –  vtx | Isolation (Iso) 7 B   Neural Net Variables

8. 8 8 Neural Net Train NN using signal MC and data sidebands for background Rather than making a cut on NN value: Combine results from several bins of NN output value Optimise choice of bins by maximising expected limit on B s decay

9. 9 Backgrounds Combinatorial background obtained by extrapolating sidebands to signal region Check this method in same sign dimuon and negative lifetime control regions B→hh backgrounds (h=  /K) evaluated using MC and fake rates measured in data

10. 10 Results BR(B s   ) < 4.3×10 -8 @ 95% CL 3.6 90 BR(B d   ) < 7.6×10 -9 @ 95% CL 6.0 90 ChannelExpectedObserved B s central4 ±13 B s extended2.08 ± 0.784 B d central5.3± 15 B d extended2.78 ± 0.783 3.7 fb -1 World best limits Will have ~x2 this dataset by end of 2011 CDF public note 9892

11. 11 B Rare Decays B      h : B +   K + B 0   K * B s    FCNC b  s  * Penguin or box processes in the Standard Model: Predicted BR(B s    )=1.61x10 -6 11 Rare B Decays: B d,s       K + /K*/  observed at Babar,Belle,CDF PRL103:171801,2009 PRD 79:031102,2009 PRD 79:011104,2009   not seen until now hep-ph/0303246 s s s b s b s s  

12. 12 Sensitive to Wilson coefficients: can be affected by new physics Forward backward asymmetry in B 0  K* decay Predictions exist for several new physics scenarios Forward Backward Asymmetry Standard Model θ = the angle of the lepton with respect to B direction in the dilepton rest frame s = q 2 /m B 2 C 7 =-C 7 SM C 9 C 10 =-C 9 C 10 SM A FB =-A FB SM

13. 13 Forward backward asymmetry in B 0  K* decay Predictions exist for several new physics scenarios 13 Forward Backward Asymmetry arXiv:0904.0770 arXiv:0804.4412 BabarBelle C 7 =-C 7 SM C 9 C 10 =-C 9 C 10 SM A FB =-A FB SM

14. 14 Rare decays: B →  K + /K *0 /  Simple topology: Vertex two muons with hadron (h=K + /K *0 /  ) Require J/  mass window  clean normalisation mode Reject J/ ,  ’ mass window  rare decays sample Measure the relative BR: Methodology 14 (J/  ) h B   Relative efficiency from MC

15. 15 Candidate invariant mass distributions 15 Results: mass distributions Number of sigma statistical significance: 9.7 8.5 6.3

16. 16 Forward backward asymmetry Unbinned likelihood fit Angular acceptances taken from MC 16 Results: Asymmetry Asymmetry as function of q 2 =mass(  ) 2 Expect zero asymmetry in B + Compatible and competitive with the B factories 4.4 fb -1 CDF public note 10047

17. 17 D  

18. 18 Highly suppressed decay Predicted BR ~ 4x10 -13 Can be enhanced in R parity violating SUSY by up to 7 orders of magnitude 18 D→  Rare Decay

19. 19 Measure branching ratio relative to control channel Measure muon fake rates in tagged D* samples Main background is from B decays with cascade D decays 19 D→  Rare Decay

20. 20 Measure branching ratio relative to control channel 20 D→  Results 0.36 fb -1 CDF public note 9226

21. 21 Summary B d,s    , D     are a powerful probe of new physics Could give first hint of new physics at the Tevatron Impacting new physics scenarios’ phase space B→  h first observation in B s mode, first measurement of asymmetry in these decays at hadron collider Data sample will be x2 by end of 2011, many more results to come! SO(10) hep-ph/0507233 Phys. Lett B624, 47, 2005 Constrained MSSM

22. 22 Back-up

23. 23 Gathered ~7.6 fb -1 to date 23 CDF

24. 24 Motivations for B→  h Search 1) First observation in B s channel 2) Tests of Standard Model branching ratios kinematic distributions (with enough statistics) Effective field theory for b  s (Operator Product Expansion) Sensitive to Wilson coefficients, C i calculable for many models (e.g. SUSY, technicolor) Decay amplitude: C 9 Dilepton mass distribution: C 7, C 9 Forward-backward asymmetry: C 10

25. 25 Searching for New Physics Two ways to search for new physics: direct searches – seek e.g. Supersymmetric particles indirect searches – test for deviations from Standard Model predictions e.g. branching ratios In the absence of evidence for new physics set limits on model parameters BR(B   1x10 -7 Trileptons l+l+ l-l- l+l+ q q Z* W+W+ q   

26. 26 Expected Background Extrapolate from data sidebands to obtain expected events Scale by the expected rejection from the likelihood ratio cut Also include contributions from charmless B decays B  hh (h=K/  ) (use measured fake rates)

27. 27 now 27 Limits on BR(B d,s   ) BR(B s   ) < 1.0×10 -7 @ 90% CL < 8.0×10 -8 @ 95% CL BR(B d   ) < 3.0×10 -8 @ 90% CL < 2.3×10 -8 @ 95% CL These are currently world best limits The future: Need to reoptimise after 1fb-1 for best results Assume linear background scaling

28. 28 B   in New Physics Models SUSY could enhance BR by orders of magnitude MSSM: BR(B   )  tan 6  may be 100x Standard Model R-parity violating SUSY: tree level diagram via sneutrino observe decay for low tan  mSUGRA: B   search complements direct SUSY searches Low tan   observation of trilepton events High tan   observation of B   Or something else!  ’ i23  i22   b s RPV SUSY A. Dedes et al, hep-ph/0207026

29. 29 Expected Background Tested background prediction in several control regions and find good agreement OS-: opposite sign muon, negative lifetime SS+: same sign muon, positive lifetime SS-: same sign muon, negative lifetime FM+: fake muon, positive lifetime

30. 30 Likelihood p.d.f.s Input p.d.f.s: Isolation Pointing angle Ct significance New plots***

31. 31 six dedicated rare B triggers using all muon chambers to |  |  1.1 Tracking capability leads to good mass resolution Use two types of muon pairs: central-central central-extension 31 CDF Central Muon Extension (0.6< |  | < 1.0) Central Muon Chambers (|  | < 0.6)

32. 32 B d,s   K + /K*/  B Rare Decays B +   K + B 0    B s     b   FCNC b  s  * Penguin or box processes in the Standard Model: Rare processes: Latest Belle measurement observed at Babar, Belle hep-ex/0109026, hep-ex/0308042, hep-ex/0503044 x10 -7  

33. 33 Motivations 1) Would be first observations in B s and  b channels 2) Tests of Standard Model branching ratios kinematic distributions (with enough statistics) Effective field theory for b  s (Operator Product Expansion) Rare decay channels are sensitive to Wilson coefficients which are calculable for many models (several new physics scenarios e.g. SUSY, technicolor) Decay amplitude: C 9 Dilepton mass distribution: C 7, C 9 Forward-backward asymmetry: C 10

34. 34 Samples (CDF) Dedicated rare B triggers in total six Level 3 paths Two muons + other cuts using all chambers to |  |  1.1 Use two types of dimuons: CMU-CMU CMU-CMX Additional cuts in some triggers:  p t (  )>5 GeV L xy >100  m mass(  )<6 GeV mass(  )>2.7 GeV

35. 35 Signal and Side-band Regions Use events from same triggers for B + and Bs(d)   reconstruction. Search region: - 5.169 < M  < 5.469 GeV - Signal region not used in optimization procedure Sideband regions: - 500MeV on either side of search region - For background estimate and analysis optimization. Search region Monte Carlo   )~24MeV

36. 36 MC Samples Pythia MC Tune A default cdfSim tcl realistic silicon and beamline pT(B) from Mary Bishai pT(b)>3 GeV && |y(b)|<1.5 – B s    (signal efficiencies) – B+  JK+   K+ (nrmlztn efncy and xchks) – B+  J  +    (nrmlztn correction)

37. 37 R. Dermisek et al., hep-ph/0304101 SO(10) Unification Model tan(  )~50 constrained by unification of Yukawa coupling All previously allowed regions (white) are excluded by this new measurement Unification valid for small M 1/2 (~500GeV) New Br(Bs   limit strongly disfavors this solution for m A = 500 GeV Red regions are excluded by either theory or experiments Green region is the WMAP preferred region Blue dashed line is the Br(Bs   ) contour Light blue region excluded by old Bs   analysis   h 2 >0.13 m  + <104GeV m h <111GeV Excluded by this new result

38. 38 Method: Likelihood Variable Choice Prob( ) = probability of B s   yields > obs (ie. the integral of the cumulative distribution) Prob( ) = exp(- /438  m) yields flat distribution reduces sensitivity to MC modeling inaccuracies (e.g. L00, SVX-z)

39. 39 Step 4: Compute Acceptance and Efficiencies Most efficiencies are determined directly from data using inclusive J/    events. The rest are taken from Pythia MC.  (B+/Bs) = 0.297 +/- 0.008 (CMU-CMU) = 0.191 +/- 0.006 (CMU-CMX)  LH (Bs): ranges from 70% for LH>0.9 to 40% for LH>0.99  trig (B+/Bs) = 0.9997 +/- 0.0016 (CMU-CMU) = 0.9986 +/- 0.0014 (CMU-CMX)  reco-  (B+/Bs) = 1.00 +/- 0.03 (CMU-CMU/X)  vtx (B+/Bs) = 0.986 +/- 0.013 (CMU-CMU/X)  reco-K (B+) = 0.938 +/- 0.016 (CMU-CMU/X) Red = From MC Green = From Data Blue = combination of MC and Data