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

作　　者：Chun-Wang Ma Xiao-Bao Wei Xi-Xi Chen Dan Peng Yu-Ting Wang Jie Pu Kai-Xuan Cheng Ya-Fei Guo Hui-Ling Wei 马春旺;魏啸宝;陈茜茜;彭丹;王玉廷;普洁;程凯旋;郭亚飞;魏慧玲(Institute of Particle and Nuclear Physics,College of Physics Henan Normal University,Xinxiang 453007,China)

机构地区：[1]Institute of Particle and Nuclear Physics,College of Physics Henan Normal University,Xinxiang 453007,China

出　　处：《Chinese Physics C》2022年第7期118-128,共11页中国物理C（英文版）

基　　金：Supported by the National Natural Science Foundation of China(11975091);the Program for Innovative Research Team(in Science and Technology)in University of Henan Province(21IRTSTHN011),China。

摘　　要：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^40 Ar to^208 Pb,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.

关 键 词：projectile fragmentation rare isotope machine learning Bayesian neural network drip line cross section radioactive nuclear beam 

分 类 号：O571[理学—粒子物理与原子核物理] TP18[理学—物理]