Generation of fusion and fusion-evaporation reaction cross-sections by two-step machine learning methods

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dc.authoridBAYRAM, Tuncay/0000-0003-3704-0818
dc.authoridAkkoyun, Serkan/0000-0002-8996-3385
dc.contributor.authorAkkoyun, Serkan
dc.contributor.authorYesilkanat, Cafer Mert
dc.contributor.authorBayram, Tuncay
dc.date.accessioned2024-10-26T18:07:53Z
dc.date.available2024-10-26T18:07:53Z
dc.date.issued2024
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractIn order to obtain cross-sections of heavy-ion fusion and fusion-evaporation reactions, artificial neural networks, cubist, random forest, support vector regression, extreme gradient boosting, and multiple linear regression machine learning approaches were used separately in this study. The outcomes from these different methods that are obtained from the training carried out with the existing experimental data in the literature were compared. Furthermore, it has been observed that a two-step process yielded better results for determining the heavy ion reaction cross-sections, after first estimating which approach would be better for which reaction. In this manner, the method for which the cross-section needs to be calculated is determined by the machine learning classification application, and predictions can be made using the machine learning regression application with the determined method. It has been concluded that the obtained results are in harmony with the experimental data and that the methods can be used safely. The obtained results are published on a web page that allows for online calculation of heavy-ion fusion and fusion-evaporation reaction cross-sections.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [121F411]
dc.description.sponsorshipThis study was funded by the Scientific and Technological Research Council of Turkey (TUBITAK) Grant No 121F411.
dc.identifier.doi10.1016/j.cpc.2023.109055
dc.identifier.issn0010-4655
dc.identifier.issn1879-2944
dc.identifier.scopus2-s2.0-85185836157
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.cpc.2023.109055
dc.identifier.urihttps://hdl.handle.net/20.500.12418/29727
dc.identifier.volume297
dc.identifier.wosWOS:001145519700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofComputer Physics Communications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectHeavy-ion
dc.subjectFusion
dc.subjectFusion-evaporation
dc.subjectNuclear reaction
dc.subjectMachine learning
dc.titleGeneration of fusion and fusion-evaporation reaction cross-sections by two-step machine learning methods
dc.typeArticle

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