Bayesian neural network evaluation method on the neutron-induced fission product yields of 232Th

However, obtaining complete isotopic yield dis tributions over a wide range of neutron energies remains a challenge. In this study, a Bayesian neural network (BNN) model was developed to predict the independent (IND) and cumulative (CUM) fission yields of 232Th under neutron irradiation at various incident energies.

To address the limited availability of experimental data for the analysis of IND mass distributions, we substituted mass-number-based yields with the yields of specific isotopes. Furthermore, physical phenomena or quantities, such as the odd-even effect and isospin, were intro duced as constraints to enhance the physical consistency of the predictions. The impact of these constraints was evaluated using mass-chain yield distributions and their dependence on energy.