论文标题
质子诱导剥落反应中片段产生的贝叶斯神经网络预测
A Bayesian-Neural-Network Prediction for Fragment Production in Proton Induced Spallation Reaction
论文作者
论文摘要
散布反应中的片段生产是各种应用程序的关键基础架构数据。基于经验参数化{\ sc spacs}，建立了贝叶斯神经网络（BNN）方法，以预测质子诱导的散布反应中的片段横截面。已经针对从中间到重核的系统的质子诱导的质子诱导的散布反应进行了系统的研究。通过学习实验测量和{\ sc spacs}预测之间的残差，BNN预测结果与测量结果非常吻合。建议建立的方法使核天体物理学，核放射光束源，加速器驱动系统和质子治疗等方面的相关研究受益。
Fragments productions in spallation reactions are key infrastructure data for various applications. Based on the empirical parameterizations {\sc spacs}, a Bayesian-neural-network (BNN) approach is established to predict the fragment cross sections in the proton induced spallation reactions. A systematic investigation have been performed for the measured proton induced spallation reactions of systems ranging from the intermediate to the heavy nuclei and the incident energy ranging from 168 MeV/u to 1500 MeV/u. By learning the residuals between the experimental measurements and the {\sc spacs} predictions, the BNN predicted results are in good agreement with the measured results. The established method is suggested to benefit the related researches in the nuclear astrophysics, nuclear radioactive beam source, accelerator driven systems, and proton therapy, etc.
