1. 16 May 2002Paul Dauncey - BaBar1 Measurements of CP asymmetries and branching fractions in B 0   +  ,  K +  ,  K + K  Paul Dauncey Imperial College, University of London For the BaBar Collaboration

2. 16 May 2002Paul Dauncey - BaBar2 CP violation arises due to complex CKM matrix; e.g. Wolfenstein parametrisation:  is third internal angle CP violation in the Standard Model CKM unitarity; represent as triangle in ,  plane V td  e -i  V ub  e -i 

3. 16 May 2002Paul Dauncey - BaBar3 Direct CP violation CP violation requires interference between at least two amplitudes with different phases, e.g. B 0  K +   B0B0 K+K+ T P CP violation gives a non-zero asymmetry: A K   (B 0  K     (B 0  K +    (B 0  K     (B 0  K +   ) In principle leads to  measurement Relative weak phase e i  – –

4. 16 May 2002Paul Dauncey - BaBar4 Indirect CP violation Mixing gives two paths if final state accessible from B 0 and B 0, e.g. B 0  J/  K 0 S B0B0 J/  K 0 S CP violation gives a time-dependent asymmetry: Amplitude = Im( e i  ) = sin2  Leads to  measurement: see talk by S. Rahatlou B0B0 T T M – – – Relative weak phase e i2 

5. 16 May 2002Paul Dauncey - BaBar5 CP violation in B 0   +   B 0   +   has both effects B0B0 ++ If both tree and penguin significant: parametrise as sin2  eff Interpretation also depends on strong amplitudes B0B0 T T P M P – – – Relative weak phase e i  No relative weak phase Relative weak phase e i2 

6. 16 May 2002Paul Dauncey - BaBar6 Experimental reality Analysis is different from B 0  J/  K 0 S … No clean  or K  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 B 0 or B 0  Use lepton charge, K charge or neural networks  Inefficiency and impurity (“dilution”) Cannot measure time directly  Time difference  t between two B decays –

7. 16 May 2002Paul Dauncey - BaBar7 The BaBar detector DIRC  PID) 144 quartz bars 11000 PMs 1.5T solenoid EMC 6580 CsI(Tl) crystals Drift Chamber 40 stereo layers Instrumented Flux Return iron / RPCs (muon / neutral hadrons) Silicon Vertex Tracker 5 layers, double sided strips e + (3.1GeV) e  (9 GeV) 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 – –

8. 16 May 2002Paul Dauncey - BaBar8 Particle identification with the DIRC Detector of Internally Reflected Cherenkov light (DIRC): essential for this analysis to distinguish K from  Reconstruct Cherenkov angle cos  C = 1/n  from rings seen in PMTs Cherenkov light transmitted down quartz bars by internal reflection

9. 16 May 2002Paul Dauncey - BaBar9 Analysis is done in two stages: Direct CP; extract branching fractions for  K  and KK and also the K  decay CP asymmetry A K   Maximum likelihood fit to kinematic/event shape quantities  Requires no tag or vertex measurement  Separate fit reduces systematic error Indirect CP; extract  CP asymmetry sin2  eff  Fix branching fractions and A K  to above results  Requires tag to determine if B 0 or B 0  Requires vertex information to find time dependence All results are preliminary Analysis overview –

10. 16 May 2002Paul Dauncey - BaBar10 B candidate mass using beam “energy substitution” m ES =  (E beam 2 – p B 2 ) in CM Select 5.2 < m ES < 5.3 GeV/c 2 Kinematic variables - m ES Signal Monte Carlo; Gaussian Background; ARGUS threshold function Depends on reconstructed momentum of B candidate, p B ~ 325 MeV Same for all signal modes Resolution dominated by beam energy uncertainty  m ES ) ~ 2.6 MeV/c 2 Signal shape from MC, background from fit

11. 16 May 2002Paul Dauncey - BaBar11 Kinematic variables -  E Background; quadratic function KK  KK B candidate energy difference from beam energy  E = E B – E beam in CM Select |  E | < 0.15 GeV Depends on masses of B decay products;  mass assumed K  and KK shifted to non-zero average; ~ 45 MeV per K Resolution dominated by tracking   E ) ~ 26 MeV Signal shape parametrised from MC; background from fit Signals: Gaussian

12. 16 May 2002Paul Dauncey - BaBar12 DIRC  c mean and resolution parametrised from data using D *+  D 0  +  (K –  + )  + decays Particle identification -  c  (  C ) = 2.2 mrad 99 2.5  K/  Separation Momentum range of decays

13. 16 May 2002Paul Dauncey - BaBar13 Background suppression - Fisher Signal Monte Carlo Background Cut on angle between B candidate and sphericity of the other tracks in the event: |cos  s | < 0.8 F - optimized linear combination of energy flow into nine cones around candidate (CLEO Fisher discriminant). Signal shape from MC, background from fit Cut Signal Monte Carlo Background

14. 16 May 2002Paul Dauncey - BaBar14 Five input variables per event; assume independent (uncorrelated) PDFs for each: m ES F Discriminate signal from background  E  C + Discriminate different signal modes  C – Eight fit parameters: four for signal, four for background N(  +    N(K +    N(  + K   N(K + K   Branching fraction maximum likelihood fit Fit directly for N( K  ) and A K   N(K +    N( K  ) (1–A K   N(  + K   N( K  ) (1+A K  

15. 16 May 2002Paul Dauncey - BaBar15 Branching fraction results Mode Yield (events) Branching Fraction (10 -6 ) K  Asymmetry, A K  No significant direct CP violation seen in B 0  K +    90% C.L. –0.14  A K   0.05 Main systematics: B ranching fractions – uncertainty in shape of  C PDF  Asymmetry - possible charge bias in track and  C reconstruction Fit results are:

16. 16 May 2002Paul Dauncey - BaBar16 Branching fraction projections Fit sample: 17585 candidates,  ~ 38% B0KB0K B 0  Likelihood projections: remove one variable from fit and cut on fit probabilities to give enhanced signal samples: Background and crossfeed

17. 16 May 2002Paul Dauncey - BaBar17 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 B 0 (tagging); use tracks whose charge carries flavour information  Use identical techniques to sin2  analysis Fit for CP violation asymmetries while holding branching fractions and K  asymmetry to previously determined values  Vary these parameters within determined errors for systematics; small effect for asymmetries

18. 16 May 2002Paul Dauncey - BaBar18 Asymmetric beam energies mean Y(4s) is boosted: Vertexing and  t (4s)  = 0.55 ++ zz t  z/c -- Fully reconstructed B  Best vertex for “other” B  t is time difference between the two decays: Resolution is dominated by the “other” B,  (  t) ~ 0.8 ps Independent of signal type Can use same resolution model as sin2  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

19. 16 May 2002Paul Dauncey - BaBar19 The signal modes depend differently on  t:  general form allows for both tree and penguin: rates  f(  t) ~ 1  S  sin(  m d  t)  C  cos(  m d  t) for B 0 (B 0 ) tag  S   Im    and C        For pure tree,  e i2  so S   sin  and C    With some penguin contribution,  C   and  S   C    sin  eff K  time dependence due to B 0 mixing: rates  f(  t) ~ 1  cos(  m d  t) for unmixed (mixed) B 0 KK general form similar to  in principle  Model with simple exponential Actual PDFs used in fit:  Diluted due to mistags  Convolved with  t resolution functions Signal yield time dependences  –

20. 16 May 2002Paul Dauncey - BaBar20 Flavour tagging Gev/c 2 m ES Fit sample: 9220 tagged candidates Separate into tag categories Lepton;  eff ~ 8% Neural Net 1;  eff ~ 2% Kaon;  eff ~ 14% Neural Net 2;  eff ~ 1% Use same flavour tagging as sin2  measurement: Untagged events (~ 33%) retained in the fit to determine background shapes

21. 16 May 2002Paul Dauncey - BaBar21 Fit results in: S  = –0.01 ± 0.37 ± 0.07 C  = –0.02 ± 0.29 ± 0.07 No significant indirect CP violation seen in B 0   +   90% C.L. –0.66  S   0.62 90% C.L. –0.54  C   0.48 Main systematic: Uncertainty in shape of  C PDF Time-dependent fit results Enhanced B   sample:  t distributions and asymmetry between mixed and unmixed events. Fit result Fitted background

22. 16 May 2002Paul Dauncey - BaBar22 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  B = 1.66 ± 0.09 ps (c.f. PDG value 1.54 ± 0.02 ps)  m d = 0.517 ± 0.062 ps –1 (c.f. PDG value 0.479 ± 0.012 ps –1 ) Time-dependent fit cross checks Enhanced B  K  sample: asymmetry between unmixed and mixed events.

23. 16 May 2002Paul Dauncey - BaBar23 Summary of BaBar preliminary results Branching fractions: B(B 0      ) = (5.4 ± 0.7 ± 0.4)  10 –6 B(B 0  K    ) = (17.8 ± 1.1 ± 0.8)  10 –6 B(B 0  K  K  )  =  1.1  10 –6 (90% C.L.) CP asymmetries; no evidence for CP violation: S  = –0.01 ± 0.37 ± 0.07 or 90% C.L. –0.66  S   0.62 C  = –0.02 ± 0.29 ± 0.07 or 90% C.L. –0.54  C   0.48 A K  = –0.05 ± 0.06 ± 0.01 or 90% C.L. –0.14  A K   0.05