Bayesian evaluation of residual production cross sections in proton induced spallation reactions

TL;DR

This paper introduces a Bayesian neural network combined with a simplified EPAX formula to predict residual cross sections in proton-induced spallation reactions across a wide energy range, offering improved extrapolation with less data.

Contribution

The study presents a novel BNN + sEPAX model for predicting energy-dependent residual cross sections in proton spallation reactions, enhancing extrapolation capabilities.

Findings

BNN + sEPAX model accurately predicts residual cross sections.

Model outperforms traditional methods in extrapolation.

Applicable across energies from tens of MeV/u to several GeV/u.

Abstract

The Bayesian neural network (BNN) method is used to construct a predictive model for fragment prediction of proton induced spallation reactions with the guidance of a simplified EPAX formula. Compared to the experimental data, it is found that the BNN + sEPAX model can reasonably extrapolate with less information compared with BNN method. The BNN + sEPAX method provides a new approach to predict the energy-dependent residual cross sections produced in proton-induced spallation reactions from tens of MeV/u up to several GeV/u.