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

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dc.authorid0000-0003-3704-0818
dc.contributor.authorBayram, Tuncay
dc.contributor.authorAkkoyun, Serkan
dc.contributor.authorKara, S. Okan
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2014
dc.departmentNiğde ÖHÜ
dc.description.abstractOne 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.
dc.identifier.doi10.1016/j.anucene.2013.07.039
dc.identifier.endpage175
dc.identifier.issn0306-4549
dc.identifier.scopus2-s2.0-84883065699
dc.identifier.scopusqualityQ1
dc.identifier.startpage172
dc.identifier.urihttps://dx.doi.org/10.1016/j.anucene.2013.07.039
dc.identifier.urihttps://hdl.handle.net/11480/4299
dc.identifier.volume63
dc.identifier.wosWOS:000327829400019
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartofANNALS OF NUCLEAR ENERGY
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectGround-state energies
dc.subjectArtificial neural network
dc.subjectHartree-Fock-Bogoliubov method
dc.titleA study on ground-state energies of nuclei by using neural networks
dc.typeArticle

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