Bayesian optimization and nonlocal effects method for α decay of superheavy nuclei based on CPPM

Abstract We combine nonlocal effects with Bayesian Neural Network (BNN) methods to enhance the prediction accuracy of α decay half-lives. The results indicate that accounting for nonlocal effects significantly impacts the half-life calculations, while the BNN method markedly improves prediction accuracy and demonstrates strong extrapolation capabilities. Furthermore, we discuss the impact of nuclear deformation (the quadrupole deformation factor β 2 ) on machine learning predictions. Through Shapley Additive Explanations (SHAP), we conducted a quantitative comparison of six input features within the BNN, revealing that the α decay energy Q α is the primary driving factor affecting the half-life T 1/2 . Leveraging the remarkable extrapolation ability of the BNN, we successfully predicted the α decay half-lives of the isotope chain ( Z = 118, 120), uncovering a significant shell effect at neutron number N = 184. For the isotopic chains ( Z = 118, 120), the predicted α decay half-lives and Q α values satisfy the Geiger-Nuttall (G-N) linear relationship. This result further confirms the predictive reliability of the proposed model.