Author:
Ma Chun-Wang,Peng Dan,Wei Hui-Ling,Wang Yu-Ting,Pu Jie
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.
Funder
National Natural Science Foundation of China
Subject
Astronomy and Astrophysics,Instrumentation,Nuclear and High Energy Physics
Cited by
21 articles.
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