Precise machine learning models for fragment production in projectile fragmentation reactions using Bayesian neural networks *

Abstract

Abstract Machine learning models are constructed to predict fragment production cross sections in projectile fragmentation (PF) reactions using Bayesian neural network (BNN) techniques. The massive learning for BNN models is based on 6393 fragments from 53 measured projectile fragmentation reactions. A direct BNN model and physical guiding BNN via FRACS parametrization (BNN + FRACS) model have been constructed to predict the fragment cross section in projectile fragmentation reactions. It is verified that the BNN and BNN + FRACS models can reproduce a wide range of fragment productions in PF reactions with incident energies from 40 MeV/u to 1 GeV/u, reaction systems with projectile nuclei from 40Ar to 208Pb, and various target nuclei. The high precision of the BNN and BNN + FRACS models makes them applicable for the low production rate of extremely rare isotopes in future PF reactions with large projectile nucleus asymmetry in the new generation of radioactive nuclear beam factories.