Determination of (p, n) reaction cross-section for various nuclei at 7.5 MeV by using machine learning models

Naima Amrani, Serkan Akkoyun,
Determination of (p, n) reaction cross-section for various nuclei at 7.5 MeV by using machine learning models,
Applied Radiation and Isotopes,
Volume 225,
2025,
112059,
ISSN 0969-8043,
https://doi.org/10.1016/j.apradiso.2025.112059.
(https://www.sciencedirect.com/science/article/pii/S096980432500404X)
Abstract: This study investigates the prediction of (p, n) reaction cross-sections for various nuclei at 7.5 MeV using machine learning models. A dataset of 91 instances, containing key nuclear properties such as mass number (A), proton number (Z), neutron number (N), and the asymmetry term ((N-Z)/A2), was utilized. Various machine learning techniques, including Random Forest, Support Vector Regression (SVR), Gradient Boosting, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), K-Nearest Neighbours, Multiple Linear Regression and Ensemble Model were employed. Model performances were evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R2 metrics. Among the models, ensemble methods, SVR, and boosting-based approaches demonstrated superior predictive capabilities, effectively capturing nonlinear relationships between nuclear properties and cross-sections. Results highlight the significance of the asymmetry term in enhancing prediction accuracy. This study underscores the potential of machine learning as a robust tool for nuclear physics applications, particularly in understanding and predicting nuclear reaction cross-sections.
Keywords: (p, n) reaction cross-section; Machine learning models; Asymmetry term