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Statistical modeling of nuclear data using artificial neural networks (ANNs) and, more recently, support vector machines (SVMs), is providing novel approaches to systematics that are complementary to phenomenological and semi-microscopic theories. We present a global model of β − -decay halflives of the class of nuclei that decay 100% by β − mode in their ground states. A fully-connected multilayered feed forward network has been trained using the Levenberg-Marquardt algorithm, Bayesian regularization, and cross-validation. The halflife estimates generated by the model are discussed and compared with the available experimental data, with previous results obtained with neural networks, and with estimates coming from traditional global nuclear models. Predictions of the new neural-network model are given for nuclei far from stability, with particular attention to those involved in r-process nucleosynthesis. This study demonstrates that in the framework of the β − -decay problem considered here, global models based on ANNs can at least match the predictive performance of the best conventional global models rooted in nuclear theory. Accordingly, such statistical models can provide a valuable tool for further mapping of the nuclidic chart.

In this work, the beta-decay halflives problem is dealt as a nonlinear optimization problem, which is resolved in the statistical framework of Machine Learning (LM). Continuing past similar approaches, we have constructed sophisticated Artificial Neural Networks (AN N s) and Support Vector Regression Machines (SV M s) for each class with even-odd character in Z and N to global model the systematics of nuclei that decay 100% by the β − -mode in their ground states. The arising large-scale lifetime calculations generated by both types of machines are discussed and compared with each other, with the available experimental data, with previous results obtained with neural networks, as well as with estimates coming from traditional global nuclear models. Particular attention is paid on the estimates for exotic and halo nuclei and we focus to those nuclides that are involved in the r-process nucleosynthesis. It is found that statistical models based on LM can at least match or even surpass the predictive performance of the best conventional models of β-decay systematics and can complement the latter.

Full understanding of nucleosynthesis via the r-process continues to be a major challenge for nuclear astrophysics. Apart from issues within astrophysical modeling, there remain significant uncertainties in the nuclear physics input, notably involving the β -decay halflives of neutron-rich nuclei. Both the element distribution on the r-process path and the time scale of the r-process are highly sensitive to β − lifetimes. Since the majority of nuclides that lie on the r-process path will not be experimentally accessible in the foreseeable future, it is important to provide accurate predictions from reliable models. Toward this end, a statistical global model of the β −decay halflife systematics has been developed to estimate the lifetimes of nuclides relevant to the r-process, in the form of a fully-connected, multilayer feedforward Artificial Neural Network (ANN) trained to predict the halflives of ground states that decay 100% by the β − mode. In predictive performance, the model can match or even surpass that of conventional models of βdecay systematics. Results are presented for nuclides situated on the r-ladders N = 50, 82 and 126 where abundances peak, as well as for others that affect abundances between peaks. Also reported are results for halflives of interesting neutron-rich nuclides on or towards the r-process path that have been recently measured. Comparison with results from experiment and conventional models is favorable.

Full understanding of nucleosynthesis via the r-process continues to be a major challenge for nuclear astrophysics. Apart from issues within astrophysical modeling, there remain significant uncertainties in the nuclear physics input, notably involving the β- decay halflives of neutron-rich nuclei. Both the element distribution on the r-process path and the time scale of the r-process are highly sensitive to β− lifetimes. Since the majority of nuclides that lie on the r-process path will not be experimentally accessible in the foreseeable future, it is important to provide accurate predictions from reliable models. Toward this end, a statistical global model of the β−-decay halflife systema- tics has been developed to estimate the lifetimes of nuclides relevant to the r-process, in the form of a fully-connected, multilayer feedforward Artificial Neural Network (ANN) trained to predict the halflives of ground states that decay 100% by the β− mode. In predictive performance, the model can match or even surpass that of conventional models of β-decay systematics. Results are presented for nuclides situated on the r-ladders N=50, 82 and 126 where abundances peak, as well as for others that affect abundances between peaks. Also reported are results for halflives of interesting neutron-rich nuclides on or towards the r-process path that have been recently measured. Comparison with results from experiment and conventional models is favorable.

No abstract