Neutron-Alpha Reaction Cross Section Determination by Machine Learning Approaches

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dc.contributor.authorAmrani, Naima
dc.contributor.authorYesilkanat, Cafer Mert
dc.contributor.authorAkkoyun, Serkan
dc.date.accessioned2025-05-04T16:47:25Z
dc.date.available2025-05-04T16:47:25Z
dc.date.issued2024
dc.departmentSivas Cumhuriyet Üniversitesi
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, alpha) 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.issn1572-9591
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85206250355
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s10894-024-00461-4
dc.identifier.urihttps://hdl.handle.net/20.500.12418/35611
dc.identifier.volume43
dc.identifier.wosWOS:001337155400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofJournal of Fusion Energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250504
dc.subjectReaction cross-section
dc.subject(n, alpha) reaction
dc.subjectMachine-learning
dc.titleNeutron-Alpha Reaction Cross Section Determination by Machine Learning Approaches
dc.typeArticle

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