Enhancing Reliability in Photonuclear Cross-Section Fitting with Bayesian Neural Networks
Enhancing Reliability in Photonuclear Cross-Section Fitting with Bayesian Neural Networks
Yu-XuanYang Long-XiangLiu YueZhang Zi-RuiHao ShengJin Qian-KunSun Gong-TaoFan Kai-JieChen Zhen-WeiWang Hang-HuaXu Hong-WeiWang
作者信息
摘要
This study investigates photonuclear reaction $(\gamma,n)$ 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 $^{159}$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.
Abstract
This study investigates photonuclear reaction $(\gamma,n)$ 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 $^{159}$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 BNNs 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 networks 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/ SLEGSKey words
Photoneutron reaction/ Bayesian neural network/ Machine learning/ Gamma source/ SLEGS引用本文复制引用
Yu-XuanYang,Long-XiangLiu,YueZhang,Zi-RuiHao,ShengJin,Qian-KunSun,Gong-TaoFan,Kai-JieChen,Zhen-WeiWang,Hang-HuaXu,Hong-WeiWang.Enhancing Reliability in Photonuclear Cross-Section Fitting with Bayesian Neural Networks[EB/OL].(2024-11-19)[2026-01-13].https://chinaxiv.org/abs/202411.00202.学科分类
原子能技术基础理论/物理学/粒子探测技术、辐射探测技术、核仪器仪表
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