A Bayesian-neural-network prediction for fragment production in proton induced spallation reaction *Supported by the National Natural Science Foundation of China (U1732135, 11975091). (December 2020)

• Record Type: Journal Article
• Authors: Ma 马, Chun-Wang 春旺; Peng 彭, Dan 丹; Wei 魏, Hui-Ling 慧玲; Wang 王, Yu-Ting 玉廷; Pu 普, Jie 洁
• Is Part Of: Chinese physics C. Volume 44:Number 12(2020:Dec.)
• Publication Date: 2020-12
• Subjects: Bayesian neural network (BNN) -- spallation reaction -- cross sections; Particles (Nuclear physics) -- Periodicals; Nuclear physics -- Periodicals; 539.76
• DOI: 10.1088/1674-1137/abb657
• Languages: English
• ISSNs: 1674-1137

Abstract

Abstract: Fragment production in spallation reactions yields key infrastructure data for various applications. Based on the empirical SPACS parameterizations, a Bayesian-neural-network (BNN) approach is established to predict the fragment cross sections in proton-induced spallation reactions. A systematic investigation has been performed for the measured proton-induced spallation reactions of systems ranging from intermediate to heavy nuclei systems and incident energies ranging from 168 MeV/u to 1500 MeV/u. By learning the residuals between the experimental measurements and SPACS predictions, it is found that the BNN-predicted results are in good agreement with the measured results. The established method is suggested to benefit the related research on nuclear astrophysics, nuclear radioactive beam sources, accelerator driven systems, proton therapy, etc.