分类: 物理学 >> 核物理学 提交时间: 2024-11-19
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.
分类: 核科学技术 >> 辐射物理与技术 提交时间: 2024-11-11
摘要: The neutron capture resonance parameters for 159Tb are crucial for validating nuclear models, nucleosynthesis during the neutron capture process, and nuclear technology applications. In this study, resonance analyses were performed for the neutron capture cross-sections of 159Tb measured at the China Spallation Neutron Source (CSNS) backscattering white neutron beamline (Back-n) facility. The resonance parameters were extracted from the R-Matrix code SAMMY and fitted to the experimental capture yield up to the 1.2 keV resolved resonance region (RRR). The average resonance parameters were determined by performing statistical analysis on the set of the resonance parameters in the RRR. These results were used to fit the measured average capture cross sections using the FITACS code in the unresolved resonance region from 2 keV to 1MeV. The contributions of partial waves l = 0, 1, 2 to the average capture cross-sections are reported.