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, cilt.63, ss.172-175, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 63
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1016/j.anucene.2013.07.039
  • Dergi Adı: ANNALS OF NUCLEAR ENERGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.172-175
  • Anahtar Kelimeler: Ground-state energies, Artificial neural network, Hartree-Fock-Bogoliubov method, IMPACT PARAMETER DETERMINATION
  • Karadeniz Teknik Üniversitesi Adresli: Hayır

Özet

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.

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.