An artificial neural network application on nuclear charge radii

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dc.authorid0000-0003-3704-0818
dc.contributor.authorAkkoyun, S.
dc.contributor.authorBayram, T.
dc.contributor.authorKara, S. O.
dc.contributor.authorSinan, A.
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2013
dc.departmentNiğde ÖHÜ
dc.description.abstractArtificial neural networks (ANN) have emerged with successful applications in nuclear physics as well as in many fields of science in recent years. In this paper, ANN have been employed on experimental nuclear charge radii. Statistical modeling of nuclear charge radii using ANN are seen to be successful. Based on the outputs of ANN we have estimated a new simple mass-dependent nuclear charge radii formula. Also, the charge radii, binding energies and two-neutron separation energies of Sn isotopes have been calculated by implementation of a new estimated formula in Hartree-Fock-Bogoliubov calculations. The results of the study show that the new estimated formula is useful for describing nuclear charge radii.
dc.identifier.doi10.1088/0954-3899/40/5/055106
dc.identifier.issn0954-3899
dc.identifier.issue5
dc.identifier.scopus2-s2.0-84876938943
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://dx.doi.org/10.1088/0954-3899/40/5/055106
dc.identifier.urihttps://hdl.handle.net/11480/4409
dc.identifier.volume40
dc.identifier.wosWOS:000317466300015
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherIOP PUBLISHING LTD
dc.relation.ispartofJOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.titleAn artificial neural network application on nuclear charge radii
dc.typeArticle

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