发表机构
Institute of Modern Physics, Chinese Academy of Sciences; School of Nuclear Science and Technology, University of Chinese Academy of Sciences; School of Nuclear Science and Technology, Lanzhou University（中国科学院近代物理研究所; 中国科学院大学核科学与技术学院; 兰州大学核科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种数据驱动的人工神经网络模型，用于精确预测核散裂反应中的中子双微分截面，并展现出强大的泛化能力，可服务于加速器驱动系统设计等应用。
AI 中文摘要
本文提出了一种数据驱动的人工神经网络（ANN）模型，用于描述核散裂反应中中子发射的双微分截面（DDCS）。研究发现，该ANN模型在预测核反应微分截面以及学习中子DDCS对弹核能量（$T_p$）、靶核（$A$和$Z$）、中子能量（$T_n$）和中子发射角（$\ heta_n$）的复杂依赖关系方面具有精确、灵活且高效的特点。模型基于包含不确定性的实验数据副本进行训练，并在ANN训练过程中考察了多种正则化方案。同时，对构建的ANN框架的输入变量进行了研究，最终为ANN模型的输入层选取了以下六个关键变量：$\ heta_{n}$、${\ m log}(T_n/T_p)$、$T_n/T_p$、${\ m log}(T_p)$、$A^{2/3}$和$N/Z$。将ANN预测结果与多个实验合作组提供的训练数据进行比较，显示出极好的一致性。进一步地，利用弹核能量、靶核和中子发射角均不同于训练数据的测试数据对所得模型进行测试，结果表明该ANN框架具有很强的预测能力和泛化能力。作为示例，针对铜靶预测并展示了中子DDCS随$T_n$、$\ heta_n$和弹核能量$T_p$的变化关系。所提出的高精度ANN模型预计将有益于加速器驱动系统（ADS）设计以及核物理、天体物理和核技术开发中的许多其他应用。
英文摘要
In this paper, we present a data-driven artificial neural network (ANN) model for describing the double differential cross sections (DDCS) of neutron emission in nuclear spallation reactions. The ANN model is found to be precise, flexible, and efficient in predicting differential cross sections of nuclear reactions and in learning the complex dependence of neutron DDCS on the projectile energy ($T_p$), target nucleus ($A$ and $Z$), neutron energy ($T_n$), and neutron emission angle ($θ_n$). The model is trained on replicas of experimental data that incorporate uncertainties. Several regularization schemes are examined during ANN training. The input variables of the constructed ANN framework are also investigated, and the following six key variables are selected for the input layer of the ANN model: $θ_{n}$, ${\rm log}(T_n/T_p)$, $T_n/T_p$, ${\rm log}(T_p)$, $A^{2/3}$, and $N/Z$. The ANN predictions are compared with training data provided by various experimental collaborations, showing excellent agreement. The resulting model is further tested on test data with projectile energies, target nuclei, and neutron emission angles different from those in the training data, indicating strong predictive power and generalization capability of the ANN framework. As an illustration, the neutron DDCS as functions of $T_n$, $θ_n$, and projectile energy $T_p$ are predicted and presented for copper target. The proposed high-precision ANN model is expected to be beneficial for accelerator-driven system (ADS) design and many other applications in nuclear physics, astrophysics, and nuclear technology development.
Comments15 pages, 13 figures
