基于Bayesian神经网络评价核裂变产额分布

核裂变碎片的产额是核能和核应用领域中的关键基础数据。在实验和理论上，获得精确且完整的能量依赖的裂变产额到目前为止都是一个挑战。贝叶斯神经网络(Bayesian Neural Networks，BNN)可以描述需要量化不确定性的逆向回归问题。基于BNN学习已有的裂变产额，可以给出未知的裂变产额及其不确定度。特别是当裂变产物产额的实验数据不完整时，BNN可以推断出完整的裂变产额。在裂变产额的质量分布和能量依赖关系上，BNN的评价结果比较合理。研究结果表明BNN在核数据评价领域有广泛的应用前景。

Evaluation of Fission Yields with Bayesian Neural Networks

Nuclear fission data are important infrastructure data in nuclear applications and nuclear engineering. It is still challenging to obtain accurate and complete energy-dependent fission yields in experiments and theories. Bayesian Neural Network (BNN) is idea to treat inverse regression problems and can provide quantified uncertainties. We apply BNN to infer fission yields based on learning of existing fission yields. In particular, BNN is very useful for evaluations of fission yields when incomplete experimental data are available. We demonstrated that the BNN evaluations are quite satisfactory on mass distributions and energy dependencies of fission yields. This indicates that BNN is very promising in nuclear data community.