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Search for New Physics through Rare B Decays S. Uozumi for the CDF-II collaboration Feb-21th Lake Louis Winter Institute 2012 NP?
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CDMS II Rouzbeh Allahverdi, Bhaskar Dutta, Yudi Santoso arXiv:0912.4329 Excluded by 1)Rare B decay b s 2)No CDM candidate 3)Muon magnetic moment a b c B s for NP (e.g.SUSY) B s for NP (e.g.SUSY) SM Expectation Br enhancement By New Physics JHEP 1009 (2010)106 Gaugeno mass (GeV) Slepton/sqqark mass (GeV)
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3 B s Method : Relative normalization counting Count signal region events : 5.169<M <5.469 GeV –corresponds to 6 σ m, where σ m ≈24MeV Sideband regions: additional +0.5 GeV –Used to understand background MC simulation of B s and B 0 → mass peaks – 9.7 fb-1 of whole CDF Run-II dataset is used – Measure the rate of B s → μ + μ - decays relative to B J/ K + – Apply same sample pre-selection criteria – B s → μ + μ - sample is highly purified with Neural Network event selection Dominant Backgrounds Sequential semi-leptonic decay: b → cμ - X → μ + μ - X Double semileptonic decay: bb → μ + μ - X Continuum μ + μ -, μ + fake, fake+fake Peaking Background in signal region (B hh)
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Control sample 4 Roughly selected with pre-selection cuts same with one for Bs pre- selection Control sample is used not only Br. Normalization, but also various checks on BG modeling. Opposite-sign B decay length < 0 (OS-) Same-sign B decay length > 0 (SS+) Same-sign B decay length < 0 (SS-) One is tagged as fake B decay length > 0 (FM+) Prediction from B d J/ K + sample Observed in B s control sample
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Further BG discrimination by Neural Network NN input variables 3D pointing angle Isolation Proper decay length Proper decay length sig. P T (Bs) P T ( ) Etc Multi-variable analysis : Neural Network Unbiased optimization based on MC signal and data sidebands Output (0 ~ 1) Discriminating variables........ Input nodes........ NeuroBayes with 14 discriminating parameters strongly distinguish signal and BG.
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6 BG shape modeling from M Sideband Events are separated in each NN output bins In each bin, continuum BG is estimated from SB polinomial fitting < 5 GeV is rejected from the fit to avoid B + neutrinos events)
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7 Di-muon mass for B d / B s signal region Completely consistent with BG For B d channel. On B s channel, slight excess can be seen in high NN value events.
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8 Summary on Br (Bs -> mumu) limit p-values assuming B s background only : 0.94% B s SM+background : 7.1% Again with SM prediction SM prediction
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9 b s for New Physics NP? Strongly suppressed in SM due to 2 nd order weak interaction New physics in a loop can change Br. Fraction and shape of those event Many channels with different spectator available to explore b s decays Tools to go beyond SM : Br fraction of several B baryons into b s process Direction, angle of outgoing particles … may differ depending on intermediate new physics model
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10 b s Br measurement - Again Relative normalization counting - Starts with di-muon trigger datasets with 6.8 fb -1 Control sample events are pre-selected by kinematical cuts Signal candidates are selected by Neural Network in addition to pre-selection for further purification Ratio of Br. Fraction of signal and control sample is determined by Signal event Yield (w/ NN cut) Control sample yield (only with pre-selection ) Pre-selection Efficiencies ratio NN cut efficiency From PDG
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11 B(Λ b 0 →Λμ + μ - ) =[1.73±0.42(stat)±0.55(syst)]×10 -6 B(B s 0 →φμ + μ - ) =[1.47±0.24±0.46]×10 -6 B(B + →K + μ + μ - ) =[0.46±0.04±0.02]×10 -6 B(B 0 →K *0 μ + μ - ) =[1.02±0.10±0.06]×10 -6 B(B 0 →K 0 μ + μ - ) =[0.32±0.10±0.02]×10 -6 B(B + →K *+ μ + μ - ) =[0.95±0.32±0.08]×10 -6 World’s first observation !! Other b s channels have comparably large statistics with B factories. Reconstructed b s events and Br. fractions
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12 Event-shape (A FB ) analysis for b s decay Now we have 6.8 fb -1 from CDF Run II !! There are various event-shape parameters to access beyond the SM (arXiv:1108.0695.)arXiv:1108.0695 Most interesting parameter is FB asymmetry of q 2 =M 2 compared with muon polar angle on B rest frame :
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13 A FB analysis with 6.8 fb -1 B→K * μ + μ - (simultaneous fit of K *0 and K *+ channels) B + →K + μ + μ - Belle result CDF provides b s A FB measurement comparable with B factories!
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B rare decays are excellent stage to search for New Physics effect. Using full of 9.7 fb-1 of CDF Run-II data, we observed : while SM predicts Summary & Conclusions Various b s decay channels are observed : B(Λ b 0 →Λμ + μ - ) =[1.73±0.42(stat)±0.55(syst)]×10 -6 B(B s 0 →φμ + μ - ) =[1.47±0.24±0.46]×10 -6 B(B + →K + μ + μ - ) =[0.46±0.04±0.02]×10 -6 B(B 0 →K *0 μ + μ - ) =[1.02±0.10±0.06]×10 -6 B(B 0 →K 0 μ + μ - ) =[0.32±0.10±0.02]×10 -6 B(B + →K *+ μ + μ - ) =[0.95±0.32±0.08]×10 -6 … world’s first!! Observed A FB is comparable with B factories, and may imply New Physics effect.
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15 Backups
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16 SM Diagrams Penguin Diagram Box Diagram 2HDM Penguin Diagram SM Expectation NP Expectation Br enhancement Rare decay : FCNCs, forbidden at tree level Powerful Probe to New Physics JHEP 1009 (2010)106
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The CDF Detector Silicon Vertex Detector (precise position measurement) Tracking Chamber (momentum measurement) 1.4 Tesla Solenoidal magnet Calorimeter (red and blue) (energy measurement) Muon Detector (yellow & blue) General purpose collider detector for many physics programme : B physics QCD / Top quark properties Electroweak physics (W, Z bosoms) Higgs / Exotic physics
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=1.0 2.0 3.0 TOF Silicon vertex (SVX-II) detector Central tracker drift chamber Time-of-flight (TOF) system EM and HAD calorimeters Muon detector Front-end, DAQ and trigger electronics The CDF Detector
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19 New Neural Network Neural Network Input Variables 3D proper decay length Isolation Pointing angle ( 3d ) Lower |p T ( )| 3D proper decay length significance Larger |d 0 ( )| Smaller |d 0 ( )| Smaller |d 0 ( )| significance Larger |d 0 ( )| significance Vertex Fitting 2 Decay length (L 3d ) 2D pointing angle ( 2d ) L xy significance |d 0 (Bs)| New 14 variable NN to increase S/B Carefully chose input variables to avoid bias for di-muon mass shape Impact parameter
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20 B s (B 0 ) Signal vs. Background From B. Casey, ICHEP2010 K p b c K BsBs c p p b b p p BsBs b p p b c Signal sequential double B→KK
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21 J/ region analysis region entries / 10 MeV/c 2 Trigger Data collected using dimuon trigger “CC”: –2 central muons “CMU”, | |<0.6, –p T >1.5 GeV –2.7<M <6.0 GeV –p T( ) + p T( ) >4 GeV “CF”: –one central, one forward muon “CMX”, 0.6<| |<1.0 –p T >2 GeV –other cuts same as above Trigger efficiency same for muons from J/ or B s (for muon of a given p T )
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22 Two-body B→ hh decays where h produces a fake muon can contribute to the background fake muons dominated by +, -,K +, K - fake rates are determined separately using D*-tagged D → K - + events Estimate contribution to signal region by: take acceptance, M hh, p T (h) from MC samples. Normalizations derived from known branching fractions convolute p T (h) with p T and luminosity-dependent -fake rates. Double fake rate ~0.04% 2) Background from two-body hadronic B decays
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23 Current experimental results from CDF & LHC
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24 Example of D 0 peaks in one bin of p T, used to extract a p T and luminosity- dependent fake rate for K + and K - Fake rates from D*-tagged D 0 → K - + events K-K- K+K+ Kaons passing muon selection: K-K- K+K+
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25 “ Smoking gun” of some Flavor Violating NP models: –ratio BR(B s → BR(B 0 → highly inform ative about whether NP violates flavor significantly or not –clear correlation between CP violating mixing pha se from B s → J/ and BR(B s → Important complementarity with direct se arches at Tevatron and LHC –Indirect searches can access even higher mass s cales than LHC COM energies New bounds on BR(B 0 → and BR(B s → are of crucial importance, and are a top priority at the Tevatron and LHC. Altmannshofer, Buras, Gori, Paradisi, Straub, Nucl.Phys.B830:17-94,2010 Probing New Physics
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26 Plenary talk A.Buras, Beauty 2011: Probing New Physics
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27 Ensemble of background-only pseudo-experiments is used to determine a p-value for a given hypothesis Determination of the p-value Log Likelihood Distribution of pseudo-experiments for background-only hypothesis for B 0 → signal window for each pseudo-experiment, we do two fits and form the log-likelihood ratio in the denominator, the “signal” is fixed to zero (I.e. we assume background only), and in the numerator s floats L(h|x) is the product of Poisson probabilities over all NN and mass bins systematic uncertainties included as nuisance parameters, modeled as Gaussian. Result: the p-value for the background-only hypothesis is 23.3% with
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