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Branching Fraction and Direct CP Violation in B 0   K 0 and B    h 

Branching Fraction and Direct CP Violation in B 0   K 0 and B    h . Alfio Lazzaro Dipartimento di Fisica and INFN, Milan on behalf of the BaBar Collaboration American Physical Society 2003, Philadelphia. Physics Motivation Analysis Strategy: Samples, Event Selection, ML fits

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Branching Fraction and Direct CP Violation in B 0   K 0 and B    h 

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  1. Branching Fraction and Direct CP Violation in B0K0and Bh Alfio Lazzaro Dipartimento di Fisica and INFN, Milan on behalf of the BaBar Collaboration American Physical Society 2003, Philadelphia Alfio Lazzaro

  2. Physics Motivation Analysis Strategy: Samples, Event Selection, ML fits Results on Branching Fractions and Charge asymmetries Conclusion Overview Alfio Lazzaro

  3. Physics Motivation Measurements of Branching Fractions allow constraints on phenomenological models Charge asymmetries (Ach) expected large (>20%) in BhK (Soni, PRL 43, 242 (1979)) and Bhp (Gronau, PRL 79, 4333 (1997)) Previous Measured Branching Fractions (106): Need precise measurements Alfio Lazzaro

  4. Data Samples and Sub-channels We have used 81.9 fb-1 on-resonance (88.9 millions of BB pairs) _ We considered 3 channels: Reconstructed in (23%) (39%) Alfio Lazzaro

  5. Event selection Beam energy to constraint B mass and energy: • Event shapes to distinguish B events from continuum udsc: • Fisher discriminant combining 4 variables: • direction of B thrust axis wrt beam direction • direction of B momentum wrt beam direction • 2 Legendre moments of the energy flow of rest of event wrt B thrust axis • |cos (qthrust)| < 0.9 • Loose cuts based on shapes variables, PID and kinematical quantities of secondary daughters, Cherenkov angle cut for h selection. We prepare the input to Maximum Likelihood Fit Alfio Lazzaro

  6. Observables • Two types of background: • qq (continuum ) dominant • bb (non continuum ) ~ 1% – 2% • Observables: E, mES, Fisher,h mass _ _ We useUnbinned Extended Maximum Likelihood (ML) Fit to extract signal yields and charge asymmetries Alfio Lazzaro

  7. Results for hK0 and hh Preliminary First observation Alfio Lazzaro

  8. Negative logLikelihood Dashed blue line: Dotted red line: Solid black line: Alfio Lazzaro

  9. mES and DE projections Shaded histograms represent h gg Alfio Lazzaro

  10. Systematics on BF and Ach Sources Error on yields • Systematics due to ML fit efficiency and bias • Daughter Branching fractions • Reconstruction efficiencies (h, p0, KS, charged tracks) • Other sources: BB Number, MC statistics, PID, … 0.3- 0.5 0.03- 0.4 0.3- 2.0 0.03- 0.6 Total systematic error on BF: 7.1% (hh+) 7.7% (hK0) Charge Asymmetries Systematic on charge dependence bias 1.1% Alfio Lazzaro

  11. hK0, hK • Branching Fraction for BhK larger than initially expected by theory • Tree diagrams are Cabibbo suppressed: • Interference between two penguin diagrams and the known h – h mixing angle conspire to greatly enhance BhK and suppress BhK (Lipkin, Phys. Lett. B 254, 247 (1991)) Alfio Lazzaro

  12. Results for hK0 and hK BF Preliminary Ach See also talk by Fred Blanc, Session T11 Alfio Lazzaro

  13. mES and DE Projections Shaded histograms represent h hpp Alfio Lazzaro

  14. Conclusion We have presented the following preliminaryresults: First observation Suppressed BhK Enhanced BhK 2.5s Alfio Lazzaro

  15. Altre Analisi in Corso B–>h' • BAD-462 Supporting Document BAD-605 Physics Note s g  _ _ t s _ b _ d  B0 W+ d d Risultati di CLEO Alfio Lazzaro

  16. B–>h' • Analisi finita da tempo • Yields negativi • Quindi abbiamo fatto una analisi di tipo Bayesiano • Si sta ancora discutendo con i referee (Bob Cahn) ed altri esperti sul modo piu’ appropriato di presentare i risultati Alfio Lazzaro

  17. B–>h’KL Supporting document : BAD-591 Scopo : Misura di BR e Time-Dependent CP-ViolatingAsymmetries ( analogo a quanto abbiamo fatto con S) Attualmente usiamo il sotto-decadimento   ma in un prossimo futuro verra’ aggiunto  ° Statistica usata : RUN 1 + RUN 2 Alfio Lazzaro

  18. B–>h’KL • Funzioni di likelihood per la identificazione delle KL (Antimo) • Constraint su MB e sulle direzioni per la ricostruzione cinematica • Analisi ML con le variabili: Mse, M , M , Fisher • Forte presenza di fondo comporta tagli su cos qT  bassa  ( 6 % ) Alfio Lazzaro

  19. B–>h’KL Ora stiamo cercando di aumentare l’efficienza di ricostruzione sostituendo alcuni tagli e il discriminante di Fisher con una rete neurale (NN). Uscita della rete usata come taglio per input ML DE , M ,M Analisi ML utilizzando le variabili : ( NN ,  ) Analisi cut & count nel piano : Control Sample: e+e-   (   S L ) Alfio Lazzaro

  20. B–>h’KS • Stiamo misurando il BR di questo canale nel sottodecadimento h h p p e h 3 p KS + - Stiamo aggiornando l’analisi a tutta la statistica (Run1 + Run2) Useremo questi dati nella prossima analisi CP-time dependent Supporting Document : BAD-488 Alfio Lazzaro

  21. B–>h , hh , hh , hh • Stiamo misurando il BR di B nei sottodecadimenti con h e h h p p (h ) Per i canali hh, hh, hh stiamo usando le varie combinazioni dei sottodecadimenti con h e h3, h e  . In queste analisi siamo ancora agli inizi! Alfio Lazzaro

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