Bayesian Neural Network Evaluation of Neutron-Induced Fission Product Yields of 232Th

摘要: Research on neutron-induced fission product yields of $^{232}$Th is crucial to understanding the competition between symmetric and asymmetric fission in actinide nuclei. However, obtaining complete isotopic yield distributions over a wide range of neutron energies remains experimentally challenging. In this work, a Bayesian neural network (BNN) model is developed to predict both independent (IND) and cumulative (CUM) fission yields of $^{232}$Th 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 yields of specific isotopes. Furthermore, physical phenomena or quantities such as the odd-even effect and isospin are introduced as constraints to enhance the physical consistency of the predictions. The impact of these constraints is evaluated through the mass-chain yield distributions and their dependence on energy. Incorporating physical constraints significantly improves the prediction accuracy, yielding more reliable and physically meaningful fission yield data for applications in nuclear physics and reactor design.