A Bayesian-Neural-Network Prediction for Fragment Production in Proton Induced Spallation Reaction

Abstract

Fragments productions in spallation reactions are key infrastructure data for various applications. Based on the empirical parameterizations {\sc spacs}, a Bayesian-neural-network (BNN) approach is established to predict the fragment cross sections in the proton induced spallation reactions. A systematic investigation have been performed for the measured proton induced spallation reactions of systems ranging from the intermediate to the heavy nuclei and the incident energy ranging from 168 MeV/u to 1500 MeV/u. By learning the residuals between the experimental measurements and the {\sc spacs} predictions, the BNN predicted results are in good agreement with the measured results. The established method is suggested to benefit the related researches in the nuclear astrophysics, nuclear radioactive beam source, accelerator driven systems, and proton therapy, etc.

7 figures, accepted by Chinese Physics C

References in corpus (6)

• Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects
• Extension of the Liège Intranuclear-Cascade model to reactions induced by light nuclei
• Nuclear charge radii: Density functional theory meets Bayesian neural networks
• Spallation Residues in the Reaction 56Fe + p at 0.3, 0.5, 0.75, 1.0 and 1.5 A GeV
• Measurement of the complete nuclide production and kinetic energies of the system 136Xe + hydrogen at 1 GeV per nucleon
• Improving proton-induced one-nucleon removal in intranuclear cascade

Cited by in corpus (5)

• Machine learning the nuclear mass
• Bayesian evaluation of charge yields of fission fragments of 239U
• Prediction of Nuclear Charge Density Distribution with Feedback Neural Network
• Optimizing multilayer Bayesian neural networks for evaluation of fission yields
• Bayesian evaluation of residual production cross sections in proton induced spallation reactions