A Bayesian-neural-network prediction for fragment production in proton induced spallation reaction

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.