Isotopic cross-sections in proton induced spallation reactions based on the Bayesian neural network method
Isotopic cross-sections in proton induced spallation reactions based on the Bayesian neural network method
复制标题
基于贝叶斯神经网络方法的质子诱发散裂反应中的同位素截面
DOI：
10.1088/1674-1137/44/1/014104
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Wada R
中科院分区：
文献类型：
--
作者：
Ma Chunwang;Peng Dan;Wei Huiling;Niu Zhongming;Wang Yuting;Wada R
提出了一种用贝叶斯神经网络（BNN）方法预测质子诱导空间反应同位素截面的方法。本文利用SPACS参数化方法，在质量为36 ~ 238，入射能量为200 ~ 1500 MeV/u的范围内，从19个实验测量和5个理论预测中得到的4000多组同位素截面数据，结果表明，BNN方法可以很好地预测Spectrometry反应中残基的碎片截面。
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.