A study on ground-state energies of nuclei by using neural networks


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Bayram T. , AKKOYUN S., KARA S. O.

ANNALS OF NUCLEAR ENERGY, vol.63, pp.172-175, 2014 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 63
  • Publication Date: 2014
  • Doi Number: 10.1016/j.anucene.2013.07.039
  • Title of Journal : ANNALS OF NUCLEAR ENERGY
  • Page Numbers: pp.172-175
  • Keywords: Ground-state energies, Artificial neural network, Hartree-Fock-Bogoliubov method, IMPACT PARAMETER DETERMINATION

Abstract

One of the fundamental ground-state properties of nuclei is binding energy. Artificial neural networks (ANN) have been performed to obtain binding energies of nuclei based on the data calculated from Hartree–Fock–Bogolibov method with two Skyrme forces SLy4 and SKP. ANN has been employed to obtain two-neutron and two-proton separation energies of nuclei. Statistical modeling of ground-state energies using ANN has been seen as to be successful in this study. Particularly, predictive power of ANN has been drawn from estimations for energies of Sr, Xe, Er and Pb isotopic chains which are not seen before by the network. The study shows that such a statistical model can be possible tool for searching in systematic of nuclei beyond existing experimental data.

One of the fundamental ground-state properties of nuclei is binding energy. Artificial neural networks (ANN) have been performed to obtain binding energies of nuclei based on the data calculated from Hartree-Fock-Bogolibov method with two Skyrme forces SLy4 and SKP. ANN has been employed to obtain two-neutron and two-proton separation energies of nuclei. Statistical modeling of ground-state energies using ANN has been seen as to be successful in this study. Particularly, predictive power of ANN has been drawn from estimations for energies of Sr, Xe, Er and Pb isotopic chains which are not seen before by the network. The study shows that such a statistical model can be possible tool for searching in systematic of nuclei beyond existing experimental data. (C) 2013 Elsevier Ltd. All rights reserved.