Neutron-alpha reaction cross section determination by machine learning approaches

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dc.contributor.authorAmrani, Naima
dc.contributor.authorYeşilkanat, Cafer Mert
dc.contributor.authorAkkoyun, Serkan
dc.date.accessioned2024-12-02T13:38:21Z
dc.date.available2024-12-02T13:38:21Z
dc.date.issued2024
dc.departmentAÇÜ, Eğitim Fakültesi, Matematik ve Fen Bilimleri Eğitimi Bölümüen_US
dc.description.abstractThis study focuses on leveraging powerful machine learning approaches to determine neutron- alpha reaction cross-sections within the 14–15 MeV energy range. The investigation utilizes an experimental dataset comprising measurements of 133 nuclei concerning (n, ?) reaction cross- sections. These data are divided into training and validation subsets, following established protocols, with 80% allocated for model training and 20% for testing. Key nucleus characteristics, including neutron number (N), mass number (A), and symmetry representation [(N-Z)²/A], were used as input variables for the machine learning models. SVR and XGBoost methods showed superior performance among the other machine learning methods used in the present study. In addition, a machine learning based online calculation tool was developed to estimate the reaction cross section.
dc.identifier.doi10.1007/s10894-024-00461-4
dc.identifier.issn0164-0313
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ2
dc.identifier.urihttp://dx.doi.org/10.1007/s10894-024-00461-4
dc.identifier.urihttps://hdl.handle.net/11494/5089
dc.identifier.volume43en_US
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Fusion Energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subject(n, α) Reactionen_US
dc.subjectMachine-Learningen_US
dc.subjectReaction Cross-Sectionen_US
dc.titleNeutron-alpha reaction cross section determination by machine learning approachesen_US
dc.typeArticle

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