Isotopic cross-sections in proton induced spallation reactions based on the Bayesian neural network method * * Supported by the National Natural Science Foundation of China (11975091, U1732135, 11875070), and Natural Science Foundation of Henan Province (162300410179). This work is also supported by the US Department of Energy (DE-FG02-93ER40773)

Abstract

The Bayesian neural network (BNN) method is proposed to predict the isotopic cross-sections in proton induced spallation reactions. Learning from more than 4000 data sets of isotopic cross-sections from 19 experimental measurements and 5 theoretical predictions with the SPACS parametrization, in which the mass of the spallation system ranges from 36 to 238, and the incident energy from 200 MeV/u to 1500 MeV/u, it is demonstrated that the BNN method can provide good predictions of the residue fragment cross-sections in spallation reactions.