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arXiv 2603.04789nucl-th
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具有量化不确定性的(n,p)反应截面的贝叶斯学习

Bayesian Learning of (n,p) Reaction Cross Sections with Quantified Uncertainties

ICFAI University Tripura(印度普拉特里帕拉 ICFAI 大学);Jožef Stefan Institute(约热·斯特凡研究所)

Arunabha Saha, Songshaptak De

AI总结:

研究针对中子诱发(n,p)反应截面预测问题,开发贝叶斯神经网络残差学习模型\texttt{BNN-R5},利用五个核描述符学习对数空间残差,经随机变分推理训练,校正截面与实验数据一致性更好,还分析出各描述符重要性。

摘要:

准确的中子诱发(n,p)反应截面对于核能、放射性核素生产、材料研究和核天体物理学等应用至关重要。然而,许多同位素的实验数据仍然稀少,评估的核数据库可能与现有测量存在系统偏差。我们开发了一种贝叶斯神经网络(BNN)残差学习模型,记为\texttt{BNN-R5},以改进(n,p)反应截面预测。该模型使用五个具有物理动机的核描述符,不采用实验或评估的截面值作为输入特征。\texttt{BNN-R5}不是直接预测截面,而是学习评估的TENDL-2023数据与实验测量之间的对数空间残差,从而为评估库提供数据驱动的校正。该模型使用随机变分推理进行训练,提供预测均值和贝叶斯不确定性估计。在广泛的靶核范围内,校正后的截面通常与实验数据显示出更好的一致性,并且优于原始的TENDL-2023评估。使用SHapley Additive exPlanations(SHAP)进行的特征重要性分析确定配对项δ是最有影响的描述符,其次是激发能变量ln(ΔE)和中子数N,而质子数Z的总体影响最小。这些结果表明,贝叶斯残差学习为改进评估的核数据和预测核图数据稀疏区域的反应截面提供了一个强大且可解释的框架。

英文摘要:

Accurate neutron-induced $(n,p)$ reaction cross sections are essential for applications in nuclear energy, radionuclide production, materials studies, and nuclear astrophysics. However, experimental data remain sparse for many isotopes, and evaluated nuclear data libraries can show systematic deviations from available measurements. We develop a Bayesian neural network (BNN) residual learning model, denoted \texttt{BNN-R5}, to improve $(n,p)$ reaction cross-section predictions. The model uses five physically motivated nuclear descriptors and does not employ experimental or evaluated cross-section values as input features. Rather than predicting the cross sections directly, \texttt{BNN-R5} learns the log-space residual between the evaluated TENDL-2023 data and experimental measurements, thereby providing a data-driven correction to the evaluated library. The model is trained using stochastic variational inference, which provides predictive mean values together with Bayesian uncertainty estimates. Across a broad range of target nuclei, the corrected cross sections generally show improved agreement with experimental data and outperform the original TENDL-2023 evaluations. Feature-importance analysis using SHapley Additive exPlanations (SHAP) identifies the pairing term $δ$ as the most influential descriptor, followed by the excitation-energy variable $\ln(ΔE)$ and the neutron number $N$, while the proton number $Z$ has the smallest overall influence. These results demonstrate that Bayesian residual learning provides a robust and interpretable framework for improving evaluated nuclear data and predicting reaction cross sections in data-sparse regions of the nuclear chart.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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