A Bayesian-neural-network prediction for fragment production in proton induced spallation reaction |
| |
Authors: | Chun-Wang Ma Dan Peng Hui-Ling Wei Yu-Ting Wang Jie Pu |
| |
Institution: | 1. Institute of Particle and Nuclear Physics, Henan Normal University, Xinxiang 453007, China2. School of Physics, Henan Normal University, Xinxiang 453007, China |
| |
Abstract: | Fragment production in spallation reactions yields key infrastructure data for various applications. Based on the empirical SPACS parameterizations, a Bayesian-neural-network (BNN) approach is established to predict the fragment cross sections in proton-induced spallation reactions. A systematic investigation has been performed for the measured proton-induced spallation reactions of systems ranging from intermediate to heavy nuclei systems and incident energies ranging from 168 MeV/u to 1500 MeV/u. By learning the residuals between the experimental measurements and SPACS predictions, it is found that the BNN-predicted results are in good agreement with the measured results. The established method is suggested to benefit the related research on nuclear astrophysics, nuclear radioactive beam sources, accelerator driven systems, proton therapy, etc. |
| |
Keywords: | Bayesian neural network (BNN) spallation reaction cross sections |
|
| 点击此处可从《中国物理C(英文版)》浏览原始摘要信息 |
| 点击此处可从《中国物理C(英文版)》下载免费的PDF全文 |
|