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Search for FCNC Decays B s(d) → μ + μ -

Search for FCNC Decays B s(d) → μ + μ -. Matthew Herndon, May 2006 University of Wisconsin Carnegie Mellon University High Energy Physics Seminar. Motivation Tevatron and CDF Analysis Method Results Conclusion. BEACH 04. J. Piedra. 1. The Standard Model.

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Search for FCNC Decays B s(d) → μ + μ -

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  1. Search for FCNC Decays Bs(d)→ μ+μ- Matthew Herndon, May 2006 University of Wisconsin Carnegie Mellon University High Energy Physics Seminar • Motivation • Tevatron and CDF • Analysis Method • Results • Conclusion BEACH 04 J. Piedra 1

  2. The Standard Model Predictions of the Standard Model have been verified to high precision • Successes of the Standard Model • Simple comprehensive theory • Explains the hundreds of common particles: atoms, protons, neutrons, electrons • Explains the interactions between them • Basic building blocs • 6 quarks: up, down... • 6 leptons: electrons... • Force carrier particles: photon... All common matter particles are composites of the quarks & leptons which interact by the exchange of force carrier particles CMU Seminar M. Herndon 2

  3. Beyond the Standard Model Standard Model fails to answer many fundamental questions Look for new physics that would explain these mysteries: SUSY, Extra Dimensions ... • Gravity not a part of the SM • What is the very high energy behaviour? • At the beginning of the universe? • Grand unification of forces? • Where is the Antimatter? • Why is the observed universe mostly matter? • Dark Matter? • Astronomical observations of gravitational effects indicate that there is more matter than we see • Why look for physics beyond the Standard Model CMU Seminar M. Herndon 3

  4. Searches For New Physics A unique window of opportunity to find new physics before the LHC • How do you search for new physics at a collider? • Direct searches for production of new particles • Particle-antipartical annihilation • Example: the top quark • Indirect searches for evidence of new particles • Within a complex decay new particles can occur virtually • Tevatron is at the energy frontier and a data volume frontier: 1 billion B and Charm events on tape • So much data that we can look for some very unusual decays • Where to look • Many weak decays of B and charm hadrons are very low probability • Look for contributions from other low probability processes – Non Standard Model CMU Seminar M. Herndon 4

  5. Bs→ μμ:Beyond the SM One of the best indirect search channels at the Tevatron • Look at decays that are suppressed in the Standard Model: Bs(d)→μ+μ- • Flavor changing neutral currents(FCNC) to leptons • No tree level decay in SM • Loop level transitions: suppressed • CKM , GIM and helicity(ml/mb): suppressed • SM: BF(Bs(d)→μ+μ-) = 3.5x10-9(1.0x10-10) G. Buchalla, A. Buras, Nucl. Phys. B398,285 • New physics possibilities • Loop: MSSM: mSugra, Higgs Doublet • 3 orders of magnitude enhancement • Rate tan6β/(MA)4 Babu and Kolda, Phys. Rev. Lett. 84, 228 • Tree: R-Parity violating SUSY CMU Seminar M. Herndon 5

  6. CDF and the Tevatron Bs(d)→μ+μ- benefits from new data - • 1.96TeV pp collider • Performance substantially improving each year • Record peak luminosity in 2006: 1.8x1032sec-1cm-2 • CDF Integrated Luminosity • ~1fb-1 with good run requirements • All critical systems operating including silicon • Analysis presented here uses 780pb-1 • Doubled data in 2005, predicted to double again in 2006 CMU Seminar 6

  7. CDF Detector EXCELLENT TRACKING TRIGGERED TO 1.5 GeV/c • Silicon Tracker • |η|<2, 90cm long, rL00 =1.3 - 1.6cm • Drift Chamber(COT) • 96 layers between 44 and 132cm • Muon coverage • |η|<1.5 • Triggered to |η|<1.0 • Outer chambers: high purity muons CMU Seminar 7

  8. Rare B Decay Physics & Triggers 1 Billion B and Charm Events on Tape TRIGGERS ARE CRITICAL - • Large production rates • σ(pp → bX, |y| < 1.0, pT(B) > 6.0GeV/c) = ~30μb, ~10μb • All heavy b states produced • B0, B+, Bs, Bc, Λb, Ξb • Backgrounds: > 3 orders of magnitude higher • Inelastic cross section ~100 mb • Challenge is to pick one B decay from >103 other QCD events • Di-muon trigger • pT(μ) > ~1.5 GeV/c, |η| < ~1.0 • B yields 2x Run I (lowered pT threshold, increased acceptance) • Di-muon triggers for rare decay physics • Bs(d)→μ+μ-, B+→μ+μ-K+, B0→μ+μ-K*0, Bs→μ+μ-φ, Λb→μ+μ-Λ • Trigger on di-muon masses from near 0GeV/c2 to the above Bs mass • Reduce rate by requiring outer muon chamber hits: pT(μ) > ~3.0 GeV/c or pTμ >5.0GeV/c 8 M. Herndon

  9. Bs→ μμ: Experimental Challenge • Primary problem is large background at hadron colliders • Analysis and trigger cuts must effectively reduce the large background around mBs = 5.37GeV/c2 to find a possible handful of events • Key elements of the analysis are determining the efficiency and rejection of the discriminating variables and estimating the background level CMU Seminar M. Herndon 9

  10. Analysis Method • Performing this measurement requires that we • Demonstrate an understanding of the background • Optimize the cuts to reduce background • Accurately estimate α and ε: for triggers, reconstruction and selection cuts • SM predicts 0 events, this is essentially a search • Emphasis on accurately predicting the Nbg and performing an unbiased optimization • Blind ourselves to the signal region • Estimate background from sidebands and test background predictions in orthogonal samples CMU Seminar M. Herndon 10

  11. Data Sample 100M Events 23K Events • Start the with di-muon trigger • 2 CMU muons or 1 CMU and 1CMX muon with pTμ >5.0GeV/c • CMU: pT(μ) > ~1.5 GeV/c, |η| < ~0.6, CMX: pT(μ) > ~2.0 GeV/c, 0.6 < |η| < 1.0 • 1 CMUP muon: pT(μ) > ~3.0 GeV/c and 1 CMU(X) muon • Apply basic quality cuts • Track, vertex and muon quality cuts • Loose preselection on analysis cuts • PT(μ+μ-) > 4.0 GeV/c, 3D Decay length significance> 2 … • In the mass region around the Bs: 4.669 < Mμμ < 5.969 GeV/c2 • Blind region: 4σ(Mμμ), 5.169 < Mμμ < 5.469 GeV/c2 • Sideband region 0.5 GeV/c2 on either side of the blinded region • Sample still background dominated • Expect < 10Bs(d)→μ+μ-events to pass these cuts: based on previous limits CMU Seminar M. Herndon 11

  12. Normalization Data Sample 9.8 X 107B+ events • Normalize to the number of BJ/K+ events • Relative normalization analysis • Some systematic uncertainties will cancel out in the ratios of the normalization • Example: muon trigger efficiency the same for J/ or Bs muons for a given pT • Apply same sample selection criteria as for Bs(d)→μ+μ- M. Herndon 12

  13. Signal vs. Background + L3D PT() - L3D di-muon vertex primary vertex + LT PT() - LT di-muon vertex 23K Events primary vertex Cut on mass, lifetime, how well pT points to the vertex and isolation • Need to discriminate signal from background • Reduce background by a factor of > 1000 • Signal characteristics • Final state fully reconstructed • Bs is long lived (cτ = 438 μm) • B fragmentation is hard: few additional tracks • Background contributions and characteristics • Sequential semi-leptonic decay: b → cμ-X→ μ+μ-X • Double semileptonic decay: bb → μ+μ-X • Continuum μ+μ- • μ + fake, fake+fake • Partially reconstructed, short lived, has additional tracks - CMU Seminar M. Herndon 13

  14. Discriminating Variables 4 primary discriminating variables • Mass Mmm • choose 2.5σwindow:σ = 25MeV/c2 • exp(λ=cτ/cτBs) • α : |φB – φvtx| in 3D • Isolation: pTB/( trk + pTB) • exp(λ), α and Iso: used in likelihood ratio • Optimization • Unbiased optimization • Based on simulated signal and data sidebands • Optimize based on likely physics result • a priori expected BF limit CMU Seminar M. Herndon 14

  15. CDF 3D Silicon Vertex Detector • 8 layer silicon detector • 4 double sided layers with stereo strips at a small angle • 3 double sided layers layers with strips at 90 • 3D vertexing is a powerful discriminant 2D  from previous analysis CMU Seminar M. Herndon 15

  16. Likelihood Ratio • Probability distributions used to construction the likelihood ratio • Use binned histograms to estimate the probabilities Resulting likelihood with signal MC and data sidebands CMU Seminar M. Herndon 16

  17. Background Estimate Analysis dependent on an accurate background estimate • Estimate the background for any given set of requirements • Typical method: apply all cuts, see how many sideband events pass • Optimal cuts may be chosen to reject 1-2 unusual events • Flat background distribution allows use of wide mass window Need to show that mass is distribution is linear Need to investigate backgrounds that may peak in signal region: Bhh Design crosschecks to double check background estimates CMU Seminar M. Herndon 17

  18. Background Estimate Optimal point looks achievable Cross-checks needed! • Expected number of combinatorial background in a 60 MeV/c2 signal window • Extrapolation from sideband • 0.99 expected to be near optimal cut, 1 background event typical of optimal search CMU Seminar M. Herndon 18

  19. Background Cross-Checks + - primary vertex Fake di-muon vertex • Use independent data samples to test background estimates • OS-: opposite sign muons, negative lifetime(signal sample is OS+) • SS+ and SS-: same sign muons, positive and negative lifetime • FM: OS- and OS+ fake μ enhanced, one μ fails the muon reconstruction quality cuts • Compare predicted vs. observed # of bg. events: 3 sets of cuts • Loose: LR > 0.5 • Medium: LR > 0.9 • Tight: LR > 0.99 CMU Seminar M. Herndon 19

  20. Bg. Cross-Checks Cont. For >0: hist: OS pts: SS [cm]   OS+ vs. OS- OS+ vs. SS+ • OS- Excellent control sample: • SS and fake muon could represent some bb backgrounds - CMU Seminar M. Herndon 20

  21. Bg. Cross-Checks Cont. • OS- and Fake μ samples • SS+,SS-: expect and find 0 events for tighter cuts • Using 150 MeV/c2 mass window for cross-check • Combined CMU-CMU(X) • Think about results with tight cuts and FM • Investigated Bhh carefully • Generated inclusive MC to look for anomalous background sources - no surprises found CMU Seminar M. Herndon 21

  22. Bhh Background Bhh background substantial! • Clearly peaks in signal region • Sideband estimates not useful • Observed/measured at CDF • Convolute known branching ratios and acceptance with K and  fake rates. (estimated for LH > 0.99) CMU Seminar M. Herndon 22

  23. Acceptance & Efficiencies From MC From MC: Checked with Data From Data From Data Second critical part of the analysis is estimating the efficiencies • :efficiency - from data where possible • Using J/ψ → μ+μ- and B+ → J/ψK+data and special unbiased J/ψ triggers • Most efficiencies relative: Exception - and LH and K CMU Seminar M. Herndon 23

  24. Example: SVX Efficiency Very low and went down with pT and time! • Measurement of the efficiency of adding silicon hits to COT tracks • Use J/ψ→ μ+μ- data (2001-2002 data) (2001-2002 data) • εSVX = 74.5 ± 0.3(stat) ± 2.2(sys)%(2001-2003 data) • Average efficiency for adding silicon to two tracks: In silicon fid. M. Herndon 24

  25. New SVX Efficiency old avg (88%) w/ same data (2003) +2% and flat - improved code +5% εSVX improved code and new quality cuts • Improved silicon pattern recognition code and detector performance εSVX = 74.5% → 88.5% 2001-2003 → 2001-2005 data CMU Seminar M. Herndon 25

  26. LR Efficiency Cross-Check Agreement good • Efficiencies determined using realistic MC • MC efficiencies validated by comparing with data using B+→ J/ψK+ events • Pt and isolation distributions reweighed to match data Same treatment for Bs • 4% systematic uncertainty assigned • 5% from isolation reweighing CMU Seminar M. Herndon 26

  27. Final Efficiencies & Acceptances Very good efficiency for large background rejection • LR efficiencies determined using realistic MC • Pt and isolation distributions reweighed to match data using Bs J/ events • For LR > 0.99 • 39% efficiency, 7% sys • ~Factor 2000 of rejection • Acceptance ratio: • a(B+/Bs) = 0.297 ± 0.006 (CMUCMU), 0.191 ± 0.008 (CMUCMX): 7% sys - generation model • Most efficiency ratios near 1 • Two absolute efficiencies: • K+COT = 95% and K+SXV = 96% CMU Seminar M. Herndon 27

  28. Optimization Results Optimized for 1fb-1 a prioriBs limit: 1.1x10-7 95% CL Previous CDF result 2.0x10-7 • Systematic uncertainties • Bg. estimation: 28% • Bg statistical error • Sensitivity: 16% • 12% fs/fu • 7% acceptance • 7% LR NB+: 5763 ± 101 εLR: 39% Backgrounds: • Tried Likelihood ratios from 0.9-0.99: LR Cut 0.99 CMU Seminar M. Herndon 28

  29. Bs(d)→ μ+μ-SearchResult Worlds Best Limits! BF(Bs +- ) < 1.0x10-7 at 95% CL BF(Bd +- ) < 3.0x10-8 at 95% CL Results: 1(2) Bs(d)candidates observedconsistent with background expectation CDF 1 Bs result: 3.010-6 CDF Bs result: 365pb-1 2.010-7 D0 Bs result: 240pb-1 4.110-7 BaBar Bd result: 8.310-8(90%) PRL 95, 221805 2005 DO Note 4733-Conf PRL 94, 221803 2005 CMU Seminar M. Herndon 29

  30. Bs→μμ: Physics Reach Looks like the theorists have a little work to do! BF(Bs +- ) < 1.0x10-7 at 95% CL • Excluded at 95% CL • BF(Bs +- ) = 1.0x10-7 • Dark matter constraints • Strongly limits specific SUSY models: SUSY SO(10) models • Allows for massive neutrino • Incorporates dark matter results R. Dermisek et al.hep-ph/0507233 R. Dermisek JHEP 0304 (2003) 037 CMU Seminar M. Herndon 30

  31. Bs→μμ: Physics Reach BF(Bs +- ) < 1.0x10-7 at 95% CL • In addition to limiting S0(10) models – starting to impact standard MSSM scenarios: CMSSM • BlueBF(Bs→ μ+μ-) • Light green:b→ s • Cyan: dark matter constraints • Dashed red:Higgs mass bound • Red brown areas:excluded • Incorporates errors from fBs and mtop J. Ellis Phys. Lett. B624, 47 2005 BF(Bs +- ) = 2.0(1.0)x10-7 CMU Seminar M. Herndon 31

  32. Conclusions BF(Bs +- ) < 1.0x10-7 at 95% CL BF(Bd +- ) < 3.0x10-8 at 95% CL Worlds Best Limits! Goal for 1fb-1 analysis: BF(Bs +- ) < 7.0x10-8 at 95% CL CDF B(s,d)→μ+μ-results • Best Bs and Bd results: well ahead of D0 and the B factories • Limit excludes part of parameter space allowed by SO(10) models • Expanding sensitivity to interesting areas of MSSM parameter space • Improving analysis to include selection NN and advanced lepton ID(from CMU: Vivek) • Improved fs results could also improve result by 10-20%(from CMU: Karen) CMU Seminar M. Herndon 32

  33. Muon & Trigger Efficiencies 75 100 75 100 trigger efficiency (%) 75 100 75 100 75 100 2 6 10 PT (GeV) • L1 Muon L1(pT,η), L3 1 number • Convolute εL1 for each muon and εL3 • Systematic errors L1 and L3: • Kinematic difference between J/ψ → μ+μ-and Bs(d)→μ+μ- • 2-Track correlations • Sample statistics • εtrig = 85 ± 3% • Offline muon reconstruction • From J/ψ → μ+μ- L1 trigger with one muon found • Systematic from comparison to Z • εmuon =95.9 ±1.3(stat) ± 0.6(sys)% • L1 eff: Use J/ψ → μ+μ- trigger that only requires one muon • L3 eff: Use autoexcept trigger M. Herndon 33

  34. COT Efficiency • Estimated by embedding COT hits from MC muons in real data • Occupancy effects correctly accounted for • Efficiency driven by loss of hits when hit density is high • Demonstrate agreement between hit usage in data and MC • Tunable parameters can lower or raise the hit usage and the efficiency to bracket the data • εCOT = 99.62 ± 0.02% • Systematic errors • Isolation dependence dominant systematic • Pt dependence • 2-track correlations • Varying the simulation tuning • Error: +0.34 -0.91 • Consistent with true tracking eff. using high pt elections identified in the calorimeter M. Herndon 34

  35. Bs→μμ: Physics Reach BF(Bs +- ) < 1.0x10-7 at 95% CL • In addition to limiting S0(10) models – starting to impact standard MSSM scenarios: mSugra • BlueBF(Bs→ μ+μ-) • Light green:b→ s • Cyan: dark matter constraints • Dashed red:Higgs mass bound • Red brown areas:excluded • Incorporates errors from fBs and mt J. Ellis Phys. Lett. B624, 47 2005 M. Herndon 35

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