Machine learning-based prediction of proton-induced reaction cross-sections for medically relevant bromine radioisotopes

Abstract

Accurate prediction of nuclear reaction cross-sections is essential for optimizing the production of medically relevant radioisotopes. In this study, we investigate proton-induced reactions on selenium isotopes leading to the formation of bromine radioisotopes (⁷⁵Br, ⁷⁶Br, ⁷⁷Br, and ^80mBr) using machine learning (ML) techniques. A total of 499 experimental data points were extracted from the EXFOR database and processed using Savitzky–Golay filtering and cubic spline interpolation to reduce noise and augment the dataset. Feature engineering incorporated both categorical and physics-informed variables to enhance model learning. Three ML models—Random Forest (RF), LightGBM, and Gaussian Process Regression (GPR)—were trained and evaluated using RMSE, R², and 10-fold cross-validation. The results show that RF consistently outperformed other models, achieving an RMSE of 16.73 mb and R² of 0.99 post-preprocessing. LightGBM also performed robustly, while GPR showed limited stability in sparse or nonlinear regions. Model performance was further analyzed by reaction type and radioisotope. The study highlights the effectiveness of ML and preprocessing in cross-section prediction under data-scarce conditions and suggests future directions including deep learning and hybrid ensemble models.