dorsal/arxiv
View SchemaA Global Model of $\beta^-$-Decay Half-Lives Using Neural Networks
| Authors | N. Costiris, E. Mavrommatis, K. A. Gernoth, J. W. Clark |
|---|---|
| Categories | |
| ArXiv ID | nucl-th/0701096 |
| URL | https://arxiv.org/abs/nucl-th/0701096 |
Abstract
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 $\beta^-$-decay halflives of the class of nuclei that decay 100% by $\beta^-$ 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 $\beta^-$-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.
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"abstract": "Statistical modeling of nuclear data using artificial neural networks (ANNs)\nand, more recently, support vector machines (SVMs), is providing novel\napproaches to systematics that are complementary to phenomenological and\nsemi-microscopic theories. We present a global model of $\\beta^-$-decay\nhalflives of the class of nuclei that decay 100% by $\\beta^-$ mode in their\nground states. A fully-connected multilayered feed forward network has been\ntrained using the Levenberg-Marquardt algorithm, Bayesian regularization, and\ncross-validation. The halflife estimates generated by the model are discussed\nand compared with the available experimental data, with previous results\nobtained with neural networks, and with estimates coming from traditional\nglobal nuclear models. Predictions of the new neural-network model are given\nfor nuclei far from stability, with particular attention to those involved in\nr-process nucleosynthesis. This study demonstrates that in the framework of the\n$\\beta^-$-decay problem considered here, global models based on ANNs can at\nleast match the predictive performance of the best conventional global models\nrooted in nuclear theory. Accordingly, such statistical models can provide a\nvaluable tool for further mapping of the nuclidic chart.",
"arxiv_id": "nucl-th/0701096",
"authors": [
"N. Costiris",
"E. Mavrommatis",
"K. A. Gernoth",
"J. W. Clark"
],
"categories": [
"nucl-th",
"astro-ph",
"cond-mat.dis-nn",
"physics.data-an"
],
"title": "A Global Model of $\\beta^-$-Decay Half-Lives Using Neural Networks",
"url": "https://arxiv.org/abs/nucl-th/0701096"
},
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