Precise machine learning models for fragment production in projectile fragmentation reactions using Bayesian neural networks
Precise machine learning models for fragment production in projectile fragmentation reactions using Bayesian neural networks
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DOI：
10.1088/1674-1137/ac5efb
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发表时间：
2022-03
影响因子：
3.6
通讯作者：
Chun-Wang Ma;Xiao-Bao Wei;Xi-Xi Chen-Xi;Peng Dan;Yuting Wang;J. Pu;Kaixuan Cheng;Ya-Fei Guo;Hui-Ling Wei
中科院分区：
文献类型：
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作者：
Chun-Wang Ma;Xiao-Bao Wei;Xi-Xi Chen-Xi;Peng Dan;Yuting Wang;J. Pu;Kaixuan Cheng;Ya-Fei Guo;Hui-Ling Wei
利用贝叶斯神经网络（BNN）技术，建立了预测弹丸碎裂（PF）反应碎片生成截面的机器学习模型。BNN模型的大规模学习是基于来自53个测量的射弹碎裂反应的6393个碎片。建立了直接BNN模型和基于FRACS参数化的物理引导BNN（BNN + FRACS）模型，用于预测弹丸碎裂反应中的碎片截面。结果表明，BNN和BNN + FRACS模型可以模拟入射能量从40 MeV/u到1 GeV/u的PF反应、入射原子核从40 Ar到208 Pb的反应体系以及各种靶核的碎片产生。BNN和BNN + FRACS模型的高精度使其适用于新一代放射性核束工厂中未来具有大射弹核不对称性的PF反应中极稀有同位素的低产生率。
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 40Ar to 208Pb, 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.