Enhancing reliability in photonuclear cross-section fitting with Bayesian neural networks  

作　　者：Qian-Kun Sun Yue Zhang Zi-Rui Hao Hong-Wei Wang Gong-Tao Fan Hang-Hua Xu Long-Xiang Liu Sheng Jin Yu-Xuan Yang Kai-Jie Chen Zhen-Wei Wang 

机构地区：[1]Shanghai Institute of Applied Physics,Chinese Academy of Sciences,Shanghai 201800,China [2]University of Chinese Academy of Sciences,Beijing 100049,China [3]Shanghai Advanced Research Institute,Chinese Academy of Sciences,Shanghai 201210,China [4]School of Physics and Microelectronics,Zhengzhou University,Zhengzhou 450001,China [5]School of Physical Science and Technology,ShanghaiTech University,Shanghai 201210,China

出　　处：《Nuclear Science and Techniques》2025年第3期146-156,共11页核技术(英文)

基　　金：supported by National key research and development program(No.2022YFA1602404);the National Natural Science Foundation of China(Nos.12388102,12275338,12005280);the Key Laboratory of Nuclear Data foundation(No.JCKY2022201C152)。

摘　　要：This study investigates photonuclear reaction(γ,n)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 159Tb,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 

分 类 号：O57[理学—粒子物理与原子核物理]