SMOTE-based data augmentation for accurate classification of neutron halo nuclei: A machine learning approach in nuclear physics
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Dosyalar
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier B.V.
Erişim Hakkı
info:eu-repo/semantics/embargoedAccess
Özet
Neutron 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.
Açıklama
Anahtar Kelimeler
Classification, Halo nucleus, Machine learning, Nuclear structure
Kaynak
Knowledge-Based Systems
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
318
