B−>πlνB -> \pi l \nu Form Factors Calculated on the Light-Front

A consistent treatment of $B\rightarrow \pi l \nu$ decay is given on the light-front. The $B$ to $\pi$ transition form factors are calculated in the entire physical range of momentum transfer for the first time. The valence-quark contribution is obtained using relativistic light-front wave functions. Higher quark-antiquark Fock-state of the $B$-meson bound state is represented effectively by the $|B^*\pi\rangle$ configuration, and its effect is calculated in the chiral perturbation theory. Wave function renormalization is taken into account consistently. The $|B^*\pi\rangle$ contribution dominates near the zero-recoil point ($q^2\simeq 25$ GeV$^2$), and decreases rapidly as the recoil momentum increases. We find that the calculated form factor $f_+(q^2)$ follows approximately a dipole $q^2$-dependence in the entire range of momentum transfer.Comment: Revtex, 19 pages, 9 figure

A simplified model of the source channel of the Leksell Gamma Knife(R)^(R): testing multisource configurations with PENELOPE

A simplification of the source channel geometry of the Leksell Gamma Knife$^{\circledR}$, recently proposed by the authors and checked for a single source configuration (Al-Dweri et al 2004), has been used to calculate the dose distributions along the $x$, $y$ and $z$ axes in a water phantom with a diameter of 160~mm, for different configurations of the Gamma Knife including 201, 150 and 102 unplugged sources. The code PENELOPE (v. 2001) has been used to perform the Monte Carlo simulations. In addition, the output factors for the 14, 8 and 4~mm helmets have been calculated. The results found for the dose profiles show a qualitatively good agreement with previous ones obtained with EGS4 and PENELOPE (v. 2000) codes and with the predictions of GammaPlan$^{\circledR}$. The output factors obtained with our model agree within the statistical uncertainties with those calculated with the same Monte Carlo codes and with those measured with different techniques. Owing to the accuracy of the results obtained and to the reduction in the computational time with respect to full geometry simulations (larger than a factor 15), this simplified model opens the possibility to use Monte Carlo tools for planning purposes in the Gamma Knife$^{\circledR}$.Comment: 13 pages, 8 figures, 5 table

Nonperturbative Determination of Heavy Meson Bound States

In this paper we obtain a heavy meson bound state equation from the heavy quark equation of motion in heavy quark effective theory (HQET) and the heavy meson effective field theory we developed very recently. The bound state equation is a covariant extention of the light-front bound state equation for heavy mesons derived from light-front QCD and HQET. We determine the covariant heavy meson wave function variationally by minimizing the binding energy $\bar{\Lambda}$. Subsequently the other basic HQET parameters $\lambda_1$ and $\lambda_2$, and the heavy quark masses $m_b$ and $m_c$ can also be consistently determined.Comment: 15 pages, 1 figur

Probabilistic rank-one tensor analysis with concurrent regularizations

Subspace learning for tensors attracts increasing interest in recent years, leading to the development of multilinear extensions of principal component analysis (PCA) and probabilistic PCA (PPCA). Existing multilinear PPCAs are based on the Tucker or CANDECOMP/PARAFAC (CP) models. Although both kinds of multilinear PPCAs have shown their effectiveness in dealing with tensors, they also have their own limitations. Tucker-based multilinear PPCAs have a restrictive subspace representation and suffer from rotational ambiguity, while CP-based ones are more prone to overfitting. To address these problems, we propose probabilistic rank-one tensor analysis (PROTA), a CP-based multilinear PPCA. PROTA has a more flexible subspace representation than Tucker-based PPCAs, and avoids rotational ambiguity. To alleviate overfitting for CP-based PPCAs, we propose two simple and effective regularization strategies, named as concurrent regularizations (CRs). By adjusting the noise variance or the moments of latent features, our strategies concurrently and coherently penalize the entire subspace. This relaxes unnecessary scale restrictions and gains more flexibility in regularizing CP-based PPCAs. To take full advantage of the probabilistic framework, we further propose a Bayesian treatment of PROTA, which achieves both automatic feature determination and robustness against overfitting. Experiments on synthetic and real-world datasets demonstrate the superiority of PROTA in subspace estimation and classification, as well as the effectiveness of CRs in alleviating overfitting