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Measurements of CP asymmetries and branching fractions in B 0 ï‚® p + p - , K + p - , K + K -

Measurements of CP asymmetries and branching fractions in B 0 ï‚® p + p - , K + p - , K + K -. Paul Dauncey Imperial College, University of London For the BaBar Collaboration. CP violation in the Standard Model. CP violation arises due to complex CKM matrix; e.g. Wolfenstein parametrisation:.

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Measurements of CP asymmetries and branching fractions in B 0 ï‚® p + p - , K + p - , K + K -

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  1. Measurements of CP asymmetries and branching fractions inB0  p+p- ,K+p-,K+K- Paul Dauncey Imperial College, University of London For the BaBar Collaboration Paul Dauncey - BaBar

  2. CP violation in the Standard Model CP violation arises due to complex CKM matrix; e.g. Wolfenstein parametrisation: CKM unitarity; represent as triangle in r,h plane Vtd e-ib Vub e-ig • is third internal angle Paul Dauncey - BaBar

  3. Direct CP violation CP violation requires interference between at least two amplitudes with different phases, e.g. B0  K+p- T Relative weak phaseeig K+p- B0 P CP violation gives a non-zero asymmetry: AKp = G(B0  K-p+)-G(B0  K+p-)/G(B0  K-p+)+G(B0  K+p-) In principle leads tog measurement – – Paul Dauncey - BaBar

  4. Indirect CP violation – Mixing gives two paths if final state accessible from B0 and B0, e.g. B0  J/Y K0S T B0 J/Y K0S Relative weak phaseei2b – T – M B0 CP violation gives a time-dependent asymmetry: Amplitude = Im(ei2b) = sin2b Leads to b measurement: see talk by S. Rahatlou Paul Dauncey - BaBar

  5. CP violation in B0  p+p- B0  p+p-has both effects T P B0 p+p- – Relative weak phaseeia P M – – T B0 No relative weak phase Relative weak phaseei2a If both tree and penguin significant: parametrise as sin2aeff Interpretation also depends on strong amplitudes Paul Dauncey - BaBar

  6. Experimental reality Analysis is different from B0  J/Y K0S… • No clean pp or Kp sample; need to determine particle type • Particle identification needed • Analyse both modes simultaneously; also include KK • No clean signal sample; high backgrounds from udsc • Branching fractions are ~ 10–6– 10–5 • Need to use kinematics and event shapes to distinguish modes • Use unbinned maximum likelihood fit to extract signals …but some elements are identical: • Need to “tag” the other B to find if B0 or B0 • Use lepton charge, K charge or neural networks • Inefficiency and impurity (“dilution”) • Cannot measure time directly • Time difference Dt between two B decays – Paul Dauncey - BaBar

  7. The BaBar detector – Pep-II delivers boosted e+e-  Y(4s)  BB, on and off the Y(4s) Integrated luminosity: 55.6 fb-1 on peak, 60.2  0.7 million BB events – 1.5T solenoid EMC 6580 CsI(Tl) crystals DIRC (PID) 144 quartz bars 11000 PMs e+ (3.1GeV) Drift Chamber 40 stereo layers e- (9 GeV) Silicon Vertex Tracker 5 layers, double sided strips Instrumented Flux Return iron / RPCs (muon / neutral hadrons) Paul Dauncey - BaBar

  8. Particle identification with the DIRC Detector of Internally Reflected Cherenkov light (DIRC): essential for this analysis to distinguish K from p Reconstruct Cherenkov angle cosqC = 1/nb from rings seen in PMTs Cherenkov light transmitted down quartz bars by internal reflection Paul Dauncey - BaBar

  9. Analysis overview Analysis is done in two stages: • Direct CP; extract branching fractions for pp, Kp and KK and also the Kp decay CP asymmetry AKp • Maximum likelihood fit to kinematic/event shape quantities • Requires no tag or vertex measurement • Separate fit reduces systematic error • Indirect CP; extract pp CP asymmetry sin2aeff • Fix branching fractions and AKpto above results • Requires tag to determine if B0 or B0 • Requires vertex information to find time dependence All results are preliminary – Paul Dauncey - BaBar

  10. Kinematic variables - mES B candidate mass using beam “energy substitution” mES = (Ebeam2 – pB2) in CM Select 5.2 < mES < 5.3 GeV/c2 • Depends on reconstructed momentum of B candidate, pB ~ 325 MeV • Same for all signal modes • Resolution dominated by beam energy uncertainty • s(mES) ~ 2.6 MeV/c2 • Signal shape from MC, background from fit Signal Monte Carlo; Gaussian Background; ARGUS threshold function Paul Dauncey - BaBar

  11. Kinematic variables - DE B candidate energy difference from beam energy DE = EB – Ebeam in CM Select | DE | < 0.15 GeV • Depends on masses of B decay products; p mass assumed • Kp and KK shifted to non-zero average; ~ 45 MeV per K • Resolution dominated by tracking • s(DE) ~ 26 MeV • Signal shape parametrised from MC; background from fit Kp KK pp Signals: Gaussian Background; quadratic function Paul Dauncey - BaBar

  12. Particle identification - c DIRC c mean and resolution parametrised from data using D*+ D0+  (K–+)+ decays s(qC) = 2.2 mrad 9s K/p Separation 2.5s Momentum range of decays Paul Dauncey - BaBar

  13. Background suppression - Fisher Cut on angle between B candidate and sphericity of the other tracks in the event: |cos qs| < 0.8 Background Signal Monte Carlo Signal Monte Carlo Background Cut F - optimized linear combination of energy flow into nine cones around candidate (CLEO Fisher discriminant). Signal shape from MC, background from fit Paul Dauncey - BaBar

  14. Branching fraction maximum likelihood fit Five input variables per event; assume independent (uncorrelated) PDFs for each: • mES • F Discriminate signal from background • DE • qC+ Discriminate different signal modes • qC– Eight fit parameters: four for signal, four for background • N(p+p-) • N(K+p-) • N(p+K-) • N(K+ K-) Fit directly for N(Kp) and AKp: N(K+p-) = N(Kp) (1–AKp)/2 N(p+K-) = N(Kp) (1+AKp)/2 Paul Dauncey - BaBar

  15. Branching fraction results Fit results are: No significant direct CP violation seen in B0  K+p- • 90% C.L. –0.14  AKp  0.05 • Main systematics: • Branching fractions – uncertainty in shape of qC PDF • Asymmetry - possible charge bias in track and qC reconstruction Paul Dauncey - BaBar

  16. Branching fraction projections Likelihood projections: remove one variable from fit and cut on fit probabilities to give enhanced signal samples: B0pp B0Kp Fit sample: 17585 candidates, e ~ 38% Background and crossfeed Paul Dauncey - BaBar

  17. Measuring indirect CP violation Extend the branching fraction analysis to extract indirect CP information: • Needs extra information on: • Time of decay (vertexing); reconstruct “other” B decay point and find time difference of B decays • Flavour of the decaying B0 (tagging); use tracks whose charge carries flavour information • Use identical techniques to sin2b analysis • Fit for CP violation asymmetries while holding branching fractions and Kp asymmetry to previously determined values • Vary these parameters within determined errors for systematics; small effect for asymmetries Paul Dauncey - BaBar

  18. Vertexing and Dt Asymmetric beam energies mean Y(4s) is boosted: Best vertex for “other” B Fully reconstructed Bpp p- (4s) Dz p+ bg = 0.55 Dt @Dz/gbc Dt is time difference between the two decays: • Resolution is dominated by the “other” B, s(Dt) ~ 0.8 ps • Independent of signal type • Can use same resolution model as sin2b analysis • Exploit mixing to measure signal performance (dilutions) and efficiencies • Signal resolution determined from large sample of fully reconstructed B’s, background shape determined from fit Paul Dauncey - BaBar

  19. Signal yield time dependences The signal modes depend differently on Dt: • ppgeneral form allows for both tree and penguin: rates • f(Dt) ~ 1 Spp sin(DmdDt) Cpp cos(DmdDt) for B0(B0) tag • Spp = 2Im(l)/(1+|l|2) and Cpp = (1-|l|2)/(1+|l|2) • For pure tree, l = ei2a so Spp = sin2a and Cpp = 0 • With some penguin contribution,Cpp  0andSpp = (1-Cpp2) sin2aeff • Kptime dependence due to B0 mixing: rates • f(Dt) ~ 1 cos(DmdDt) for unmixed (mixed) B0 • KK general form similar to pp in principle • Model with simple exponential • Actual PDFs used in fit: • Diluted due to mistags • Convolved with Dt resolution functions –  Paul Dauncey - BaBar

  20. Flavour tagging Use same flavour tagging as sin2b measurement: Lepton;eeff ~ 8% Fit sample: 9220 tagged candidates Kaon; eeff ~ 14% Separate into tag categories mES Gev/c2 Neural Net 1;eeff ~ 2% Neural Net 2;eeff ~ 1% Untagged events (~ 33%) retained in the fit to determine background shapes Paul Dauncey - BaBar

  21. Time-dependent fit results Fit results in: Spp = –0.01 ± 0.37 ± 0.07 Cpp = –0.02 ± 0.29 ± 0.07 No significant indirect CP violation seen in B0  p+p- • 90% C.L. –0.66  Spp  0.62 • 90% C.L. –0.54  Cpp  0.48 Main systematic: • Uncertainty in shape of qC PDF Fit result Enhanced B pp sample: Dt distributions and asymmetry between mixed and unmixed events. Fitted background Paul Dauncey - BaBar

  22. Time-dependent fit cross checks Fit checked using “toy” studies of simulated experiments • All parameters unbiased • All errors consistent with expectations • Likelihood value (goodness of fit) consistent with expectations Fit holds B lifetime and mixing constant: cross check by fitting • tB = 1.66 ± 0.09 ps (c.f. PDG value 1.54 ± 0.02 ps) • Dmd = 0.517 ± 0.062 ps–1 (c.f. PDG value 0.479 ± 0.012 ps–1) Enhanced B  Kpsample: asymmetry between unmixed and mixed events. Paul Dauncey - BaBar

  23. Summary of BaBar preliminary results Branching fractions: B(B0  p+p-) = (5.4 ± 0.7 ± 0.4)  10–6 B(B0  K+p-) = (17.8 ± 1.1 ± 0.8)  10–6 B(B0  K+K-)=  1.1  10–6 (90% C.L.) CP asymmetries;no evidence for CP violation: Spp = –0.01 ± 0.37 ± 0.07 or 90% C.L. –0.66  Spp  0.62 Cpp = –0.02 ± 0.29 ± 0.07 or 90% C.L. –0.54  Cpp  0.48 AKp = –0.05 ± 0.06 ± 0.01 or 90% C.L. –0.14  AKp  0.05 Paul Dauncey - BaBar

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