Systematics on ground-state energies of nuclei within the neural networks
Nuclear Theory
2013-09-02 v1
Abstract
One of the fundamental ground-state properties of nuclei is binding energy. In this study, we have employed artificial neural networks (ANNs) to obtain binding energies based on the data calculated from Hartree-Fock-Bogolibov (HFB) method with the two SLy4 and SKP Skyrme forces. Also, ANNs have been employed to obtain two-neutron and two-proton separation energies of nuclei. Statistical modeling of nuclear data using ANNs has been seen as to be successful in this study. Such a statistical model can be possible tool for searching in systematics of nuclei beyond existing experimental nuclear data.
Keywords
Cite
@article{arxiv.1301.2407,
title = {Systematics on ground-state energies of nuclei within the neural networks},
author = {Tuncay Bayram and Serkan Akkoyun and S. Okan Kara},
journal= {arXiv preprint arXiv:1301.2407},
year = {2013}
}
Comments
7 pages, 6 figures