Isotopic cross-sections in proton induced spallation reactions based on the Bayesian neural network method

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 whi...

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Bibliographic Details
Published in:Chinese physics C 2020-01, Vol.44 (1), p.14104
Main Authors: Ma, Chun-Wang, Peng, Dan, Wei, Hui-Ling, Niu, Zhong-Ming, Wang, Yu-Ting, Wada, R.
Format: Article
Language:English
Subjects:
Physics
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Summary: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.
ISSN:1674-1137
2058-6132
0254-3052
DOI:10.1088/1674-1137/44/1/014104