Consistent Empirical Physical Formulas for Potential Energy Curves of 38-66Ti Isotopes by Using Neural Networks

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dc.authoridBAYRAM, Tuncay/0000-0003-3704-0818
dc.contributor.authorAkkoyun, S.
dc.contributor.authorBayram, T.
dc.contributor.authorKara, S. O.
dc.contributor.authorYildiz, N.
dc.date.accessioned2024-11-07T13:32:29Z
dc.date.available2024-11-07T13:32:29Z
dc.date.issued2013
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractNuclear shape transition has been actively studied in the past decade. In particular, the understanding of this phenomenon from a microscopic point of view is of great importance. Because of this reason, many works have been employed to investigate shape phase transition in nuclei within the relativistic and nonrelativistic mean field models by examining potential energy curves (PECs). In this paper, by using layered feed-forward neural networks (LFNNs), we have constructed consistent empirical physical formulas (EPFs) for the PECs of Ti38-66 calculated by the Hartree-Fock-Bogoliubov (HFB) method with SLy4 Skyrme forces. It has been seen that the PECs obtained by neural network method are compatible with those of HFB calculations.
dc.identifier.doi10.1134/S1547477113060022
dc.identifier.endpage534
dc.identifier.issn1547-4771
dc.identifier.issn1531-8567
dc.identifier.issue6
dc.identifier.startpage528
dc.identifier.urihttps://doi.org/10.1134/S1547477113060022
dc.identifier.urihttps://hdl.handle.net/11480/15452
dc.identifier.volume10
dc.identifier.wosWOS:000420842900007
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherPleiades Publishing Inc
dc.relation.ispartofPhysics of Particles and Nuclei Letters
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectQuantum Phase-Transitions
dc.subjectPossible E(5) Symmetry
dc.subjectShape Evolution
dc.subjectGround-State
dc.subjectMo Isotopes
dc.titleConsistent Empirical Physical Formulas for Potential Energy Curves of 38-66Ti Isotopes by Using Neural Networks
dc.typeArticle

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