1. Search for the decay B c  B s  Masato Aoki September 13, 2006 1.B s  J/  channel 1.Pion reconstruction efficiency 2.Cut variables and optimization 2.First check B s  D s ,D s   data

2. Pion(from B c ) reconstruction & vertexing efficiency B c  B s , B s  J/  Monte Carlo :10,000,000 events generated –Reconstruct only B s meson 54487 events –Reconstruct pion as well VtxFit Prob(  2 r-  )>0.1%, Si+COT hits 31819 events  58 % (just reconstructed : 39613 events : 73%)

3. Pre-selection dataset : xpmm0d, 0h and 0i with v13 goodrun list (1.1fb -1 ) p T > 0.35 GeV for all tracks Number of Silicon r-  hit layers >= 3 for all tracks Number of COT axial hit layers (with minimum 5 hits) >=3 for  and  tracks Muons – p T > 1.5 GeV,  2 stub < 9 for CMU muons – p T > 2 GeV,  2 stub < 9 for CMX muons –  (  ) < 120degree Mass window –  mass window : 10 MeV around PDG  mass – J/  mass window : 50 MeV around PDG J/  mass (mass constraint) – B s mass window : 30 MeV around 6.33601 GeV (mass constraint) Vertex –Prob(  2 r-  ) > 0.1 % for the B s and B c vertex fitting

4. Variables to be optimized p T (B c ), p T (B s ), p T (J/  ), p T (  ), p T (  ) Impact parameter of B c : d xy  R(B s,  ) ct(B c ), ct(B s  B c ), ct(B s  PV) Signal : Monte Carlo (pre-selection : 11 events expected) Background : data (sideband events) Maximize S / (1.5 + Sqrt(B)) N-1 iteration method Cleanup 30 MeV B s  mass window (corresponding to 3  )

5. [GeV/c]

6. [cm]

8. [GeV/c]

9. [cm]

11. [GeV/c]

15. Optimized cut variables p T (B c ) > 4.15 GeV p T (B s ) > 0 GeV, p T (J/  ) > 0 GeV p T (  ) > 1.49 GeV p T (  ) > 1.25 GeV |d xy (B c )| < 77  m  R(B s,  ) < 0.801 50  m < ct(B c ) < 1740  m -550  m < ct(B s  B c ) < 4060  m 200  m < ct(B s  PV) < 3620  m Expected events in 30 MeV B s  mass window  expected S/N = 2.37 / 1.17

16. Blind Box : 6.2765 ± 3  [0.005(uncertainty)+0.005(resolution)]

17. B s  D s , D s   stripped dataset : chdl07, skbh01 (no one uses those datasets) Obtained ~1000 B s, this should be ~1500(mixing analysis) What’s wrong? Bug? Trigger? Selection? Goodrun? Fit modeling?

18. Summary Cut optimization was performed –Expected events : S/N = 2.37 / 1.17 –Are there much better variables? –Likelihood? Neural Network? Looked at B s  D s ,D s   stripped dataset –A large disagreement was observed