BaBar-France meeting, 16.10.2008 LPNHE, Paris B+->D(*)+K(*)0 D. Derkach ( ) , V. Sordini, A. Stocchi (LAL) BaBar-France meeting, 16.10.2008 LPNHE, Paris

Outline motivation of the study; analysis strategy; recent results; perspectives.

Gamma measurement (from CKM08) g = 88 ± 16 ([41,123] @ 95% Prob.) g = -92 ± 16 ([-139,-57] @ 95% Prob.) Sensitivity on g crucially depends on the value of the r parameter

Today I’ll concentrate on annihilation.. The amplitudes for B->DK decays can be written in terms of common parameters (Assuming SU(2) symmetry) Vcb Vub Unknowns:

What if?.. The two ratios are correlated

rB+->rB0 Using the branching ratios and assuming rB value we can predict rB0, neglecting annihilation Example with the results pre-ICHEP08 Annihilation can play a role in  giving different rB+ for different modes (DK,D*K,DK*)  different predictions and errors for the corresponding rB0  It is also interesting per se.. see following

Annihilation can be neglected ? { b d B+ d K0 u s

{ { } Decays proceeding only through annihilation diagrams: Some measurements have been already performed Vcb b { c s l2  (fB/m) ~l2  l2 B0 s K+ d u d b B0 { c Vcb Vus u s } K0 l2  l Br(DsK) = (2.8 ± 0.5) × 10-5 Br(D0K) = (5.2 ± 0.7) × 10-5 Br(D*sK) = (2.2 ± 0.5) × 10-5 Br(D*0K) = (3.6 ± 0.7) × 10-5 Br(D0K*0) = (4.2 ± 0.6) × 10-5 l2  ln n~1 ? l2 or l ? Dominated by something else ?

{ Indeed we can have large final states rescattering u s Vus d b c Vcb K+ B0 s d d Large RESCATTERING Amplitudes B. Blok, M. Gronau, J. Rosner, Phys.Rev.Lett.78:3999-4002,1997

{ { l3  l l3  (fB/m) ~l3  l2 ~l3  l In the charged B->DK system. Question b { u Vub c l3  l B+ Vcs s K+ u u c { b l3  (fB/m) ~l3  l2 u B+ u K+ u If dominated by rescattering s ~l3  l Considering the previous case we can say that: Typical order of magnitude of a Color suppressed Vub mediated (we have to use B0) Br(B0 D0 K0 ) = (rB )2( 5.2 ±0.7) 10-5 ~ 5 10-6 rB ~ 0.3

We can measure annihilation diagram related to the previous one by SU(2): { b d B+ d K0 u s c d Vus s b { u Vcb K0 B+ d u u Following the previous arguments : Br(B+ D+ K0 ) = can be up to 5 10-6 Let’s try to measure it

Previous Analysis Previous analysis: F. Polci, R. Faccini, C. Voena BAD 830, 1035: Negative fluctuation : N = -2.9 +- 6.6 expected limit 7*10-6

Choice of Channels Branching ratio of the signal is normalized to 5*10-6 Channels Used Efficiency in previous analysis Efficiency after reconstruction Matched after reconstruction Nevents rest for analysis, assuming BR=5*10-6 D+-->K0sp+ (18.8±1.4)% (49.3±0.5)% (41.1±0.5)% (3.3±0.2) D+-->K0sp+p0 (38.1±0.4)% (21.4±0.3)% (8.3±0.5) D+-->K-p+p+ (18.4±0.5)% (50.5±0.5)% (41.7±0.5)% (31.3±1.3) D+-->K-p+p+p0 (40.8±0.5)% (20.2±0.3)% (9.5±0.5)

Strategy preselection; cut optimization; Fisher discriminant; peaking background; parameterization; Toy MC studies; Combination of channels.

20K each secondary decay channel Data Sets Sample Number of Events Lumi, fb-1 On-resonance data - Run1-Run6 Signal MC 20K each secondary decay channel 80K-1000K B0B0bar MC 736M 1337 B+B- MC 731M 1329 ccbar MC 1132M 871 uds MC 938M 449 Dedicated peaking background >2000

Cut Optimization Maximizing

Cut Table (Example of for B->DK, D->Kpp) signal error efficiency B+B- B0B0bar ccbar uds Drho Dpi D0K0 D*0K*0 S 1 preselecton 30.8 0.34 40.9% 10811.9 9009.1 190707.8 136149.7 581.3 95.3 70.2 41.0 0.052 2 PK > 0.2, Pp >0.15 29.6 0.33 39.4% 9737.6 8075.7 178663.4 127507.9 545.2 80.6 56.9 36.4 3 |MD-MPDG|<0.012 25.0 0.31 33.3% 1714.3 1956.7 39570.1 22330.7 357.3 20.2 9.1 7.5 0.097 4 |DEB|<0.02 0.28 26.9% 487.2 561.5 11399.2 6410.4 102.3 7.8 2.9 2.3 0.147 5 |cosQB_cm|<0.76 18.9 0.27 25.2% 398.9 460.1 9022.3 5089.7 86.1 5.9 1.6 0.154 6 |MK_s-MPDG|<0.006 17.6 0.26 23.5% 181.6 217.5 5396.0 2938.7 36.3 2.0 0.188 7 log(aKs +1)<-8 17.1 0.25 22.7% 53.3 79.0 3559.9 1733.4 7.2 1.0 0.232 8 |cosqHelr|<0.8 14.1 0.23 18.7% 36.6 55.6 2812.8 1352.6 1.1 0.7 0.215 9 mes>5.27 4.9 9.8 355.0 197.7 0.6 0.0 1.3 0.582 10 Fisher>0. 9.5 0.19 12.6% 5.2 43.4 23.3 0.3 1.024

Fisher Fisher discriminant was trained with the same set of observables for all the channels: Peaking background

Parameterization of mes Continuum background. Argus BBbar background. Argus Signal Gaussian BBbar peaking background (before some cuts) Crystal ball

Parameterization of Fisher Continuum background. Double Bifurcated Gaussian BBbar background. Gaussian Signal Double Bifurcated Gaussian BBbar peaking background (before some cuts) Gaussian

ToyMC studies Free parameters of the fit: Nsig, Nbbbar, Ncontinuum, Shape of the Argus function for continuum background Continuum Mean 0.00 RMS 1.01 Argus Shape Mean -0.01 RMS 1.01 BBbar Mean 0.02 RMS 1.00

Number of generated events = 14.2 Average error = 11.2 events Mean 14.0 Signal Mean -0.05 RMS 1.02

Sensitivity for Kpipi channel With 0 generated events we get an error of 3.8*10-6

The complete analysis has been done for other channels. The details are not given D+->K0sp+ signal efficiency B+B- B0B0bar ccbar uds Drho Dpi D0K0 D*0K*0 s Cuts 1,7 21,3% 3,9 8,8 403,5 248,0 0,3 1,3 0,0 0,066 mes>5.27 1,6 2,6 47,9 34,9 0,7 0,181 Fisher>0 1,2 14,7% 1,0 8,5 0,291 D+->K0sp+p0 signal efficiency B+B- B0B0bar ccbar uds Drho Dpi D0K0 D*0K*0 Drho-pi0 Dpi-pi0 s Cuts 2,4 6,2% 20,3 29,9 1390,2 843,9 1,0 0,0 0,3 0,050 mes>5.27 6,5 4,6 149,3 95,9 0,7 0,148 Fisher>0 1,9 4,9% 2,3 21,5 22,3 0,253 D+->K-p+p+p0 signal efficiency B+B- B0B0bar ccbar uds Drho Dpi D0K0 D*0K*0 Drho-pi0 Dpi-pi0 S Cuts 2,6 5,5% 97,5 139,2 5112,4 2573,5 1,3 0,0 3,3 0,030 mes>5.27 12,4 21,5 600,2 291,6 0,7 1,6 1,0 0,086 Fisher>0 1,9 4,1% 9,2 12,7 92,9 26,2 0,161

Combination of ToyMC With 0 generated events we get an error of 3*10-6 To compare with ~5.5 10-6 of the previous analysis

Tagging Categories? Could we improve our sensitivity ? Lepton Kaon 1 KaonPion+Pion+Other+No tag

Lepton Tagging Category signal B+B- B0B0bar ccbar uds Drho Dpi D0K0 D*0K*0 S 1 preselecton 3,0 157,7 181,4 1303,3 387,6 20,1 0,3 2,6 0,067 2 PK > 0.2, Pp >0.15 2,9 142,7 1224,9 362,4 18,5 2,0 3 |MD-MPDG|<0.012 2,5 25,5 55,6 302,6 58,1 13,7 0,0 1,0 0,115 4 |DEB|<0.02 6,9 17,6 86,9 25,2 3,6 0,168 5 |cosQB_cm|<0.76 1,9 5,9 13,3 70,9 18,4 3,5 0,174 6 |MK_s-MPDG|<0.006 1,7 4,9 49,9 12,6 0,9 0,202 7 log(aKs +1)<-8 1,3 2,3 34,0 8,7 0,2 0,241 8 |cosqHelr|<0.8 1,4 0,7 1,6 26,0 0,227 9 mes>5.27 1,5 0,662 10 Fisher>0. 0,814 Notice that for the lepton category no background is expected… ( and about 1 event at Br = 5 10-6)

Kaon1 Tagging Category mes>5.27 Fisher>0. signal B+B- B0B0bar ccbar uds Drho Dpi D0K0 D*0K*0 S 1 preselecton 3,6 457,1 533,2 11257,9 10186,3 35,3 5,5 3,9 0,024 2 PK > 0.2, Pp >0.15 3,4 409,4 475,7 10540,8 9539,0 32,8 4,9 2,9 3 |MD-MPDG|<0.012 68,7 123,9 2248,6 1631,7 21,0 0,7 1,0 0,0 0,045 4 |DEB|<0.02 2,3 16,7 30,2 647,7 480,6 5,0 0,068 5 |cosQB_cm|<0.76 2,2 14,4 24,4 520,3 394,4 0,3 0,071 6 |MK_s-MPDG|<0.006 2,0 5,9 11,4 306,6 263,5 1,6 0,083 7 log(aKs +1)<-8 1,9 4,2 195,7 176,3 0,5 0,099 8 |cosqHelr|<0.8 151,3 129,8 0,096 9 mes>5.27 23,0 21,3 0,240 10 Fisher>0. 1,1 2,5 0,525

Kaon2 Tagging Category preselecton signal B+B- B0B0bar ccbar uds Drho Dpi D0K0 D*0K*0 S 1 preselecton 5,5 1570,7 1358,1 25383,0 20259,1 88,9 18,9 12,4 8,8 0,025 2 PK > 0.2, Pp >0.15 5,2 1400,5 1211,8 23750,7 18909,4 83,1 14,6 10,4 8,1 3 |MD-MPDG|<0.012 4,4 246,1 289,0 5026,0 3199,4 54,0 2,0 1,6 0,047 4 |DEB|<0.02 3,6 68,1 77,7 1418,7 919,5 14,9 2,9 0,3 0,072 5 |cosQB_cm|<0.76 3,4 54,3 61,1 1139,5 742,2 12,3 2,6 0,075 6 |MK_s-MPDG|<0.006 3,2 28,1 30,6 674,1 442,8 5,7 0,7 0,093 7 log(aKs +1)<-8 3,1 8,2 13,0 424,9 282,0 0,9 0,115 8 |cosqHelr|<0.8 2,5 4,6 336,1 217,0 0,2 0,0 0,106 9 mes>5.27 1,0 45,9 29,1 0,286 10 Fisher>0. 1,7 0,544

All Other Tagging Categories signal B+B- B0B0bar ccbar uds Drho Dpi D0K0 D*0K*0 S 1 preselecton 18,7 8782,7 7088,2 155017,7 107020,1 447,8 74,1 53,0 28,6 0,035 2 PK > 0.2, Pp >0.15 18,0 7901,2 6344,3 145141,5 100197,0 420,3 63,1 42,6 24,7 3 |MD-MPDG|<0.012 15,2 1378,6 1494,0 32090,8 17498,7 270,0 14,6 5,2 5,9 0,066 4 |DEB|<0.02 12,3 395,9 436,3 9255,5 4991,9 78,9 4,9 1,6 2,0 0,100 5 |cosQB_cm|<0.76 11,5 324,6 361,2 7296,5 3939,6 66,6 3,3 1,3 0,105 6 |MK_s-MPDG|<0.006 10,7 145,9 170,7 4368,3 2221,7 28,1 0,129 7 log(aKs +1)<-8 10,4 40,9 59,5 2907,2 1268,3 5,7 0,7 0,158 8 |cosqHelr|<0.8 8,5 29,1 40,6 2301,5 999,0 0,9 1,0 0,146 9 mes>5.27 4,3 284,6 146,3 0,5 0,0 0,398 10 Fisher>0. 2,9 35,5 19,4 0,3 0,685

Perspectives Interest of measuring annihilation in Vub mediated processes. An error at 3*10-6 can be obtained for BR(B+  D+K0). Progress/work to do : The error can be further improved (flavor tagging) Analysis on data Analysis for other channels D+K*0 and D*+K0

Backup

Choice of Channels Branching ratio of the signal is normalized to 5*10-6 Channels Used Efficiency in previous analysis Efficiency after reconstruction Real events after reconstruction Nevents rest for analysis, assuming BR=5*10-6 D*+-->D0p+ D0-->K-p+ (18.9±0.9)% (40.0±0.4)% (36.3±0.4)% (7.5±0.3) D0-->K-p+p0 (6.79±0.3)% (26.4±0.4)% (19.8±0.3)% (14.3±0.8) D0-->K-p+p+p- (10.5±0.5)% (29.6±0.4)% (23.6±0.3)% (9.8±0.3) D0-->Ks0p+p- (10.8±1.0)% (24.0±0.3)% (19.9±0.3)% (2.1±0.2)

Choice of Channels Branching ratio of the signal is normalized to 5*10-6 Channels Used Efficiency after reconstruction Real events after reconstruction Nevents rest for analysis, assuming BR=5*10-6 D+-->K0sp+ (39.3±0.4)% (30.8±0.4)% (4.8±0.2) D+-->K0sp+p0 (29.8±0.4)% (15.9±0.3)% (12±1) D+-->K-p+p+ (41.7±0.5)% (27.0±0.4)% (39±1) D+-->K-p+p+p0 (34.6±0.4)% (15.1±0.3)% (13.8±0.8)

Method of Combination for ToyMC 1. Generate one ToyMC 2. 3. 4. Produce a lot of ToyMC and produce a combination

{ { } } } Idea, Vcb channel u b B+ c Vcb Vus s K+ u b B+ c Vcb Vus s

Idea, Vcb channel } d b B0 { c Vcb Vus u s } K+

Idea, Vcb channel d b B0 { c Vcb Vus u s } K0

Idea, SU(2) symmetry

Idea

Idea SU(2)

Idea SU(2)

Equations & Unknowns Unknowns: We solve these equation in Bayesian approach using flat priors for T, C, f and assuming the branching ratios to be Gaussian.

Results

Result

Idea, Vub channel b { u Vub c Vcs s B+ K+ u u u { b c B+ s K+ u u

Idea, Vub channel d { b c B+ s K0 u d

Idea, Vub channel u } { b Vub c Vcs } B+ s K0 d d

Idea

Putting all together Unknowns: