SMOTE-based data augmentation for accurate classification of neutron halo nuclei: A machine learning approach in nuclear physics

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dc.authorid0000-0002-7508-7548
dc.authorid0000-0002-8996-3385
dc.contributor.authorYeşilkanat, Cafer Mert
dc.contributor.authorAkkoyun, Serkan
dc.date.accessioned2025-06-22T17:27:06Z
dc.date.available2025-06-22T17:27:06Z
dc.date.issued2025
dc.departmentAÇÜ, Eğitim Fakültesi, Matematik ve Fen Bilimleri Eğitimi Bölümü
dc.description.abstractNeutron halo nuclei exhibit unique structural features—such as extended matter distributions, large interaction cross sections, and unusually low separation energies—that offer valuable insights into the nature of nuclear forces, stability limits, and astrophysical nucleosynthesis. Traditional analytical methods face challenges in accurately characterizing these exotic systems, highlighting the need for advanced computational techniques. In this study, we propose a machine learning–based framework that leverages Synthetic Minority Over-sampling Technique (SMOTE)-based data augmentation to address class imbalance in neutron halo classification tasks. A comprehensive evaluation is conducted on eight widely used algorithms—AdaBoost, XGBoost, C5.0, Generalized Linear Models (GLM), k-Nearest Neighbors (kNN), Naive Bayes, Random Forest, and Support Vector Machines (SVM)—, assessing their predictive performance and computational efficiency. The results demonstrate that AdaBoost and XGBoost provide superior accuracy and stability, offering a robust approach to identifying potential neutron halo candidates. Additionally, we develop an interactive Shiny application for real-time classification, thereby strengthening the connection between data-driven methodologies and nuclear structure research. Overall, this work underscores the importance of data augmentation in nuclear physics and highlights the potential of machine learning-driven strategies for the automated identification of exotic halo nuclei, paving the way for more in-depth exploration of nuclear stability and structure.
dc.identifier.doi10.1016/j.knosys.2025.113580
dc.identifier.issn09507051
dc.identifier.scopus2-s2.0-105003286332
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/11494/5593
dc.identifier.volume318
dc.identifier.wosWOS:001480481600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorYeşilkanat, Cafer Mert
dc.institutionauthorAkkoyun, Serkan
dc.institutionauthorid0000-0002-7508-7548
dc.institutionauthorid0000-0002-8996-3385
dc.language.isoen
dc.publisherElsevier B.V.
dc.relation.ispartofKnowledge-Based Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectClassification
dc.subjectHalo nucleus
dc.subjectMachine learning
dc.subjectNuclear structure
dc.titleSMOTE-based data augmentation for accurate classification of neutron halo nuclei: A machine learning approach in nuclear physics
dc.typeArticle

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