9 research outputs found
A Global Model of -Decay Half-Lives Using Neural Networks
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 -decay
halflives of the class of nuclei that decay 100% by 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
-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.Comment: Proceedings of the 16th Panhellenic Symposium of the Hellenic Nuclear
Physics Societ
Statistical Global Modeling of Beta-Decay Halflives Systematics Using Multilayer Feedforward Neural Networks and Support Vector Machines
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 (ANNs) and Support Vector Regression
Machines (SVMs) for each class with even-odd character in Z and N to global
model the systematics of nuclei that decay 100% by the beta-minus-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 beta-decay systematics and can complement the latter.Comment: 8 pages, 1 fiqure, Proceedings of the 17th HNPS Symposiu