Decoding Beta-Decay Systematics: A Global Statistical Model for Beta^- Halflives

TL;DR

This paper develops advanced neural network models to predict beta^- decay half-lives of nuclei, demonstrating improved accuracy over traditional models, especially for nuclei far from stability, aiding nuclear physics research and r-process nucleosynthesis.

Contribution

It introduces a novel application of Bayesian-regularized neural networks with improved training algorithms for nuclear beta-decay half-life prediction, surpassing prior models in accuracy.

Findings

Neural network models match or outperform traditional models in predicting beta-decay half-lives.

Models show strong predictive power for nuclei far from stability, relevant to r-process.

Advanced training algorithms improve generalization and accuracy of nuclear decay predictions.

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 improved generalization, in application to the problem of reproducing and predicting the halflives of nuclear ground states that decay 100% by the beta^- mode. More specifically, fully-connected, multilayer feedforward 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 the r-process nucleosynthesis. It is found that the new statistical models can match or even surpass the predictive performance of conventional models for beta-decay systematics and accordingly should provide a valuable additional tool for exploring the expanding nuclear landscape.