Decodingβ-decay systematics: A global statistical model forβ−half-lives

Abstract

Statistical modeling of nuclear data provides a novel approach to nuclear systematics complementary to established theoretical and phenomenological approaches based on quantum theory. Continuing previous studies in which global statistical modeling is pursued within the general framework of machine learning theory, we implement advances in training algorithms designed to improve generalization, in application to the problem of reproducing and predicting the half-lives of nuclear ground states that decay $100%$ by the ${\ensuremath{\beta}}^{\ensuremath{-}}$ mode. More specifically, fully connected, multilayer feed-forward artificial neural network models are developed using the Levenberg-Marquardt optimization algorithm together with Bayesian regularization and cross-validation. The predictive performance of models emerging from extensive computer experiments is compared with that of traditional microscopic and phenomenological models as well as with the performance of other learning systems, including earlier neural network models as well as the support vector machines recently applied to the same problem. In discussing the results, emphasis is placed on predictions for nuclei that are far from the stability line, and especially those involved in $r$-process nucleosynthesis. It is found that the new statistical models can match or even surpass the predictive performance of conventional models for $\ensuremath{\beta}$-decay systematics and accordingly should provide a valuable additional tool for exploring the expanding nuclear landscape.