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

The Standard Model 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 Predictions of the Standard Model have been verified to high precision CMU Seminar M. Herndon 2

Beyond the Standard Model Why look for physics beyond the Standard Model Standard Model fails to answer many fundamental questions 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 Look for new physics that would explain these mysteries: SUSY, Extra Dimensions ... CMU Seminar M. Herndon 3

Searches For New Physics 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 A unique window of opportunity to find new physics before the LHC CMU Seminar M. Herndon 4

Bs → μμ: Beyond the SM 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 One of the best indirect search channels at the Tevatron CMU Seminar M. Herndon 5

CDF and the Tevatron - 1.96TeV pp collider CDF Integrated Luminosity 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 Bs(d) → μ+μ- benefits from new data CMU Seminar 6

CDF Detector Silicon Tracker Drift Chamber(COT) Muon coverage |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 EXCELLENT TRACKING TRIGGERED TO 1.5 GeV/c CMU Seminar 7

Rare B Decay Physics & Triggers 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 - TRIGGERS ARE CRITICAL 1 Billion B and Charm Events on Tape M. Herndon 8

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

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

Data Sample Start the with di-muon trigger Apply basic quality cuts 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 < 10 Bs(d) → μ+μ- events to pass these cuts: based on previous limits 100M Events 23K Events CMU Seminar M. Herndon 11

Normalization Data Sample 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- 9.8 X 107 B+ events M. Herndon 12

Signal vs. Background - + PT() - L3D + PT() - LT 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 + - L3D primary vertex di-muon vertex PT() 23K Events LT primary vertex di-muon vertex + - PT() - Cut on mass, lifetime, how well pT points to the vertex and isolation CMU Seminar M. Herndon 13

Discriminating Variables 4 primary discriminating variables Mass Mmm choose 2.5σ window: 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

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

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

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

Predicted Background CMU-CMX Background Estimate 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 LH cut Predicted Background CMU-CMU Predicted Background CMU-CMX LH > 0.90 13.3±1.3 10.4±1.1 LH > 0.98 1.0±0.5 1.0±0.3 LH > 0.99 0.7±0.3 0.4±0.2 Optimal point looks achievable Cross-checks needed! CMU Seminar M. Herndon 18

Background Cross-Checks 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 + - primary vertex Fake di-muon vertex CMU Seminar M. Herndon 19

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

Bg. Cross-Checks Cont. OS- and Fake μ samples SS+,SS-: expect and find 0 events for tighter cuts OS- Predicted Observed Probability LR > 0.50 840±16 821 28% LR > 0.90 118±6 136 7% LR > 0.99 8.7±1.9 17 2% SS- LR > 0.50 8.7±1.7 6 27% SS+ LR > 0.50 11.0±1.8 12 40% 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 FM Predicted Observed Probability LR > 0.50 221±8 196 7% LR > 0.90 40±3 29 6% LR > 0.99 5.1±1.1 9 12% CMU Seminar M. Herndon 21

B  hh Background Clearly peaks in signal region Sideband estimates not useful Observed/measured at CDF Mode N Bs Mass Window N Bd Mass Window Bd   0.008 0.095 Bd  K 0.005 0.55 Bd  KK Bs   0.035 0.006 Bs  K 0.048 0.061 Bs  KK 0.089 0.66 Total 0.19±0.06 1.37±0.16 Convolute known branching ratios and acceptance with K and  fake rates. (estimated for LH > 0.99) B  hh background substantial! CMU Seminar M. Herndon 22

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

Example: SVX Efficiency Measurement of the efficiency of adding silicon hits to COT tracks Use J/ψ → μ+μ- data (2001-2002 data) (2001-2002 data) Very low and went down with pT and time! 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

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

LR Efficiency Cross-Check 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 LR Cut eLR(data) eLR(MC) Ratio LR>0.90 0.70±0.01 0.71±0.01 0.98 LR>0.92 0.68±0.01 1.00 LR>0.96 0.60±0.01 0.61±0.01 LR>0.98 0.52±0.01 0.53±0.01 LR>0.99 0.41±0.01 0.43±0.01 0.96 Agreement good 4% systematic uncertainty assigned 5% from isolation reweighing CMU Seminar M. Herndon 26

Final Efficiencies & Acceptances LR efficiencies determined using realistic MC Pt and isolation distributions reweighed to match data using Bs  J/ events Very good efficiency for large background rejection LR Cut eLR(CMUCMU) eLR(CMUCMX) LR>0.90 0.65±0.01 0.68±0.01 LR>0.95 0.57±0.01 0.61±0.01 LR>0.98 0.47±0.01 0.49±0.01 LR>0.99 0.37±0.01 0.41±0.01 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

Combinatoric Background Optimization Results Tried Likelihood ratios from 0.9-0.99: LR Cut 0.99 Optimized for 1fb-1 NB+: 5763 ± 101 εLR: 39% Backgrounds: Systematic uncertainties Bg. estimation: 28% Bg statistical error Sensitivity: 16% 12% fs/fu 7% acceptance 7% LR Decay Bhh Background Combinatoric Background Total Background Bs 0.19±0.06 1.08±0.36 1.27±0.36 Bd 1.37±0.16 2.45±0.39 a priori Bs limit: 1.1x10-7 95% CL Previous CDF result 2.0x10-7 CMU Seminar M. Herndon 28

Bs(d) → μ+μ- Search Result Decay Total Background Observed Bs 1.27±0.36 1 Bd 2.45±0.39 2 Results: 1(2) Bs(d) candidates observed consistent with background expectation BF(Bs  +- ) < 1.0x10-7 at 95% CL BF(Bd  +- ) < 3.0x10-8 at 95% CL Worlds Best Limits! 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

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

Bs → μμ: Physics Reach In addition to limiting S0(10) models – starting to impact standard MSSM scenarios: CMSSM Blue BF(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  +- ) < 1.0x10-7 at 95% CL BF(Bs  +- ) = 2.0(1.0)x10-7 CMU Seminar M. Herndon 31

Conclusions CDF B(s,d) → μ+μ- results BF(Bs  +- ) < 1.0x10-7 at 95% CL BF(Bd  +- ) < 3.0x10-8 at 95% CL Worlds Best Limits! 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) Goal for 1fb-1 analysis: BF(Bs  +- ) < 7.0x10-8 at 95% CL CMU Seminar M. Herndon 32

Muon & Trigger Efficiencies L1 eff: Use J/ψ → μ+μ- trigger that only requires one muon L3 eff: Use autoexcept trigger 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)% trigger efficiency (%) 75 100 75 100 2 6 10 PT (GeV) M. Herndon 33

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

Bs → μμ: Physics Reach In addition to limiting S0(10) models – starting to impact standard MSSM scenarios: mSugra Blue BF(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 BF(Bs  +- ) < 1.0x10-7 at 95% CL M. Herndon 35