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Statistical modeling of nuclear data provides a novel approach to nuclearsystematics complementary to established theoretical and phenomenologicalapproaches based on quantum theory. Continuing previous studies in which globalstatistical modeling is pursued within the general framework of machinelearning theory, we implement advances in training algorithms designed toimproved generalization, in application to the problem of reproducing andpredicting the halflives of nuclear ground states that decay 100% by the beta^-mode. More specifically, fully-connected, multilayer feedforward artificialneural network models are developed using the Levenberg-Marquardt optimizationalgorithm together with Bayesian regularization and cross-validation. Thepredictive performance of models emerging from extensive computer experimentsis compared with that of traditional microscopic and phenomenological models aswell as with the performance of other learning systems, including earlierneural network models as well as the support vector machines recently appliedto the same problem. In discussing the results, emphasis is placed onpredictions for nuclei that are far from the stability line, and especiallythose involved in the r-process nucleosynthesis. It is found that the newstatistical models can match or even surpass the predictive performance ofconventional models for beta-decay systematics and accordingly should provide avaluable additional tool for exploring the expanding nuclear landscape.

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