Interpretable Machine Learning for Proton-induced Neutron Reaction Cross-sections Prediction

The accurate prediction of proton-induced neutron \((p, n)\) reaction cross sections is critical for applications in nuclear engineering, medical isotope production, and astrophysics. This study introduces a machine learning framework that combines high-predictive accuracy with interpretability and uncertainty quantification. We integrate the Hiking Optimization Algorithm (<span class="sf">HOA</span>) with the eXtreme Gradient Boosting (<span class="sf">XGBoost</span>) model and employ Monte Carlo (MC) Dropout for uncertainty estimation. The framework is interpreted using SHapley Additive exPlanations (<span class="sf">SHAP</span>). First, <span class="sf">HOA</span> is utilized to navigate the high-dimensional hyperparameter space, optimizing the <span class="sf">XGBoost</span> model for performance. Subsequently, the trained model’s predictions are benchmarked against experimental data from the EXFOR database and theoretical calculations from <span class="sf">TALYS 2.0</span>. Our <span class="sf">HOA–XGBoost</span> model demonstrates better predictive accuracy over other machine learning models and provides predictions closer to experimental values than <span class="sf">TALYS 2.0</span>. The inclusion of MC Dropout provides uncertainty bounds for the model’s predictions. A detailed <span class="sf">SHAP</span> analysis reveals the underlying physical drivers of the model’s decisions: the incident proton energy (\(EN\)) is identified as the most influential feature, with its strong interaction with the reaction \(Q\)-value and product proton number (\(Z_2\)) highlighting the model’s ability to learn fundamental concepts such as reaction thresholds and the Coulomb barrier. The product nuclide’s neutron (\(N_2\)) and proton (\(Z_2\)) numbers also show influence related to nuclear stability, while the product mass number (\(A_2\)) has a lesser impact. This work presents a complementary methodology for nuclear data evaluation, paving the way for more reliable predictions and targeted experimental design. Abstract Published by the Jagiellonian University 2025 authors