Enhancing reliability in photonuclear cross-section fitting with Bayesian neural networks
2025-03
发表期刊NUCLEAR SCIENCE AND TECHNIQUES (IF:3.6[JCR-2023],2.4[5-Year])
ISSN1001-8042
EISSN2210-3147
卷号36期号:3
发表状态已发表
DOI10.1007/s41365-024-01611-1
摘要

This study investigates photonuclear reaction (gamma,n)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\gamma ,\text {n})$$\end{document} cross-sections using Bayesian neural network (BNN) analysis. After determining the optimal network architecture, which features two hidden layers, each with 50 hidden nodes, training was conducted for 30,000 iterations to ensure comprehensive data capture. By analyzing the distribution of absolute errors positively correlated with the cross-section for the isotope 159\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>{159}$$\end{document}Tb, as well as the relative errors unrelated to the cross-section, we confirmed that the network effectively captured the data features without overfitting. Comparison with the TENDL-2021 Database demonstrated the BNN's reliability in fitting photonuclear cross-sections with lower average errors. The predictions for nuclei with single and double giant dipole resonance peak cross-sections, the accurate determination of the photoneutron reaction threshold in the low-energy region, and the precise description of trends in the high-energy cross-sections further demonstrate the network's generalization ability on the validation set. This can be attributed to the consistency of the training data. By using consistent training sets from different laboratories, Bayesian neural networks can predict nearby unknown cross-sections based on existing laboratory data, thereby estimating the potential differences between other laboratories' existing data and their own measurement results. Experimental measurements of photonuclear reactions on the newly constructed SLEGS beamline will contribute to clarifying the differences in cross-sections within the existing data.

关键词Photoneutron reaction Bayesian neural network Machine learning Gamma source SLEGS
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收录类别SCI ; EI
语种英语
资助项目National key research and development program[2022YFA1602404] ; National Natural Science Foundation of China[
WOS研究方向Nuclear Science & Technology ; Physics
WOS类目Nuclear Science & Technology ; Physics, Nuclear
WOS记录号WOS:001421771800003
出版者SPRINGER SINGAPORE PTE LTD
EI入藏号20250817931061
EI主题词Multilayer neural networks
EI分类号1101 Artificial Intelligence - 1301.1.3 Atomic and Molecular Physics - 1301.2.1 High Energy Physics - 1301.2.1.1.1 Hadron Colliders - 1301.2.2 Nuclear Physics
原始文献类型Journal article (JA)
文献类型期刊论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/493522
专题物质科学与技术学院
物质科学与技术学院_博士生
通讯作者Sun, Qian-Kun; Zhang, Yue; Wang, Hong-Wei
作者单位
1.Chinese Acad Sci, Shanghai Inst Appl Phys, Shanghai 201800, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Shanghai Adv Res Inst, Shanghai 201210, Peoples R China
4.ZhengZhou Univ, Sch Phys & Microelect, Zhengzhou 450001, Peoples R China
5.ShanghaiTech Univ, Sch Phys Sci & Technol, Shanghai 201210, Peoples R China
推荐引用方式
GB/T 7714
Sun, Qian-Kun,Zhang, Yue,Hao, Zi-Rui,et al. Enhancing reliability in photonuclear cross-section fitting with Bayesian neural networks[J]. NUCLEAR SCIENCE AND TECHNIQUES,2025,36(3).
APA Sun, Qian-Kun.,Zhang, Yue.,Hao, Zi-Rui.,Wang, Hong-Wei.,Fan, Gong-Tao.,...&Wang, Zhen-Wei.(2025).Enhancing reliability in photonuclear cross-section fitting with Bayesian neural networks.NUCLEAR SCIENCE AND TECHNIQUES,36(3).
MLA Sun, Qian-Kun,et al."Enhancing reliability in photonuclear cross-section fitting with Bayesian neural networks".NUCLEAR SCIENCE AND TECHNIQUES 36.3(2025).
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