Hadronic B Decays to Charmed Baryons

We study exclusive B decays to final states containing a charmed baryon within the pole model framework. Since the strong coupling for $\Lambda_b\bar B N$ is larger than that for $\Sigma_b \bar BN$, the two-body charmful decay $B^-\to\Sigma_c^0\bar p$ has a rate larger than $\bar B^0\to\Lambda_c^+\bar p$ as the former proceeds via the $\Lambda_b$ pole while the latter via the $\Sigma_b$ pole. By the same token, the three-body decay $\bar B^0\to\Sigma_c^{++}\bar p\pi^-$ receives less baryon-pole contribution than $B^-\to\Lambda_c^+\bar p\pi^-$. However, because the important charmed-meson pole diagrams contribute constructively to the former and destructively to the latter, $\Sigma_c^{++}\bar p\pi^-$ has a rate slightly larger than $\Lambda_c^+\bar p\pi^-$. It is found that one quarter of the $B^-\to \Lambda_c^+\bar p\pi^-$ rate comes from the resonant contributions. We discuss the decays $\bar B^0\to\Sigma_c^0\bar p\pi^+$ and $B^-\to\Sigma_c^0\bar p\pi^0$ and stress that they are not color suppressed even though they can only proceed via an internal W emission.Comment: 25 pages, 6 figure

Parity-even and Parity-odd Mesons in Covariant Light-front Approach

Decay constants and form factors for parity-even (s-wave) and parity-odd (p-wave) mesons are studied within a covariant light-front approach. The three universal Isgur-Wise functions for heavy-to-heavy meson transitions are obtained.Comment: 3 pages, talk given at the 2004 DPF Meeting, Riverside, CA. Aug 26-31, 200

AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks

Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance sampling algorithm, AIS-BN, that shows promising convergence rates even under extreme conditions and seems to outperform the existing sampling algorithms consistently. Three sources of this performance improvement are (1) two heuristics for initialization of the importance function that are based on the theoretical properties of importance sampling in finite-dimensional integrals and the structural advantages of Bayesian networks, (2) a smooth learning method for the importance function, and (3) a dynamic weighting function for combining samples from different stages of the algorithm. We tested the performance of the AIS-BN algorithm along with two state of the art general purpose sampling algorithms, likelihood weighting (Fung and Chang, 1989; Shachter and Peot, 1989) and self-importance sampling (Shachter and Peot, 1989). We used in our tests three large real Bayesian network models available to the scientific community: the CPCS network (Pradhan et al., 1994), the PathFinder network (Heckerman, Horvitz, and Nathwani, 1990), and the ANDES network (Conati, Gertner, VanLehn, and Druzdzel, 1997), with evidence as unlikely as 10^-41. While the AIS-BN algorithm always performed better than the other two algorithms, in the majority of the test cases it achieved orders of magnitude improvement in precision of the results. Improvement in speed given a desired precision is even more dramatic, although we are unable to report numerical results here, as the other algorithms almost never achieved the precision reached even by the first few iterations of the AIS-BN algorithm