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

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dc.contributor.authorAkkoyun, Serkan
dc.contributor.authorYeşilkanat, Cafer Mert
dc.contributor.authorBayram, Tuncay
dc.date.accessioned2024-11-26T05:54:27Z
dc.date.available2024-11-26T05:54:27Z
dc.date.issued2024
dc.departmentAÇÜ, Eğitim Fakültesi, Matematik ve Fen Bilimleri Eğitimi Bölümüen_US
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.identifier.doi10.1016/j.cpc.2023.109055
dc.identifier.issn0010-4655
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1016/j.cpc.2023.109055
dc.identifier.urihttps://hdl.handle.net/11494/5012
dc.identifier.volume297en_US
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofComputer Physics Communications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectFusionen_US
dc.subjectFusion-Evaporationen_US
dc.subjectHeavy-ionen_US
dc.subjectMachine Learningen_US
dc.subjectNuclear Reactionen_US
dc.titleGeneration of fusion and fusion-evaporation reaction cross-sections by two-step machine learning methodsen_US
dc.typeArticle

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