Estimation of fission barrier heights for even-even superheavy nuclei using machine learning approaches

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dc.authoridAkkoyun, Serkan/0000-0002-8996-3385
dc.contributor.authorYesilkanat, Cafer Mert
dc.contributor.authorAkkoyun, Serkan
dc.date.accessioned2024-10-26T18:05:49Z
dc.date.available2024-10-26T18:05:49Z
dc.date.issued2023
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractWith the fission barrier height information, the survival probabilities of super-heavy nuclei can also be reached. Therefore, it is important to have accurate knowledge of fission barriers, for example, the discovery of super-heavy nuclei in the stability island in the super-heavy nuclei region. In this study, five machine learning techniques, Cubist model, Random Forest, support vector regression, extreme gradient boosting and artificial neural network were used to accurately predict the fission barriers of 330 even-even super-heavy nuclei in the region 140 <= N <= 216 with proton numbers between 92 and 120. The obtained results were compared both among themselves and with other theoretical model calculation estimates and experimental results. According to the results obtained, it was concluded that the Cubist model, support vector regression and extreme gradient boosting methods generally gave better results and could be a better tool for estimating fission barrier heights.
dc.identifier.doi10.1088/1361-6471/acbaaf
dc.identifier.issn0954-3899
dc.identifier.issn1361-6471
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85150892232
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1088/1361-6471/acbaaf
dc.identifier.urihttps://hdl.handle.net/20.500.12418/29190
dc.identifier.volume50
dc.identifier.wosWOS:000954181200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
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.subjectfission barrier
dc.subjectsuper-heavy nuclei
dc.subjectextreme gradient boosting
dc.subjectrandom forest
dc.subjectsupport vector regression
dc.subjectcubist
dc.subjectartificial neural network
dc.titleEstimation of fission barrier heights for even-even superheavy nuclei using machine learning approaches
dc.typeArticle

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