深度神经网络学习快中子截面  

Learning Fast Neutron Cross Section by Deep Neural Network

作　　者：胡泽华[1] 应阳君[1] 勇珩[1] 续瑞瑞[2] HU Zehua;YING Yangjun;YONG Heng;XU Ruirui(Institute of Applied Physics and Computational Mathematics,Beijing 100094,China;China Institute of Atomic Energy,Beijing 102413,China)

机构地区：[1]北京应用物理与计算数学研究所,北京100094 [2]中国原子能科学研究院,北京102413

出　　处：《原子能科学技术》2023年第4期812-817,共6页Atomic Energy Science and Technology

基　　金：国家重点研发计划(2022YFA1004500);国家自然科学基金委员会-中国工程物理研究院NSAF联合基金(U2230208);核数据重点实验室基金(JCKY2022201C155)。

摘　　要：为探索利用机器学习方法辅助核反应数据评价的可行性,采用全连接深度神经网络算法学习中子截面数据,并考察预测能力。采用通用评价核数据库中快中子区的中子截面作为数据集,训练神经网络模型并进行验证和测试。提取ENDF/B-Ⅷ.0库中铀的12个同位素^(230~241)U的快中子区中子总截面和弹性散射截面,将^(230)U的截面作为待预测的测试数据,将^(232)U的截面作为验证数据,其余10个核素的截面作为训练数据。为获得具有预测能力的神经网络模型,利用训练数据训练系列神经网络模型,再利用验证数据挑选最优模型用于预测测试数据。验证和测试显示,通过训练,神经网络模型能够较好地反映评价库中截面数据随核素、入射中子能量的变化规律,对未知核素的中子截面数据表现出较强的预测能力。因此,神经网络算法有潜力成为核数据评价的新途径。Nuclear data,especially neutron nuclear data,are the basic data for nuclear science and engineering application.Traditional nuclear data evaluation is time-consuming and labor-intensive,and is easily influenced by human factors.Machine learning technology is expected to enhance the ability of nuclear data evaluation.In order to explore the feasibility of using machine learning method to assist nuclear reaction data evaluation,a fully connected deep neural network algorithm was used to learn neutron cross section data to obtain the trained model,and the test data were used to examine the prediction ability of the model.In order to avoid the complexity of resonant region cross section,the neutron cross sections in the fast neutron region in the general evaluation nuclear library were used as the data set,with which the neural network model was trained,verified and tested.The total neutron cross sections and elastic scattering cross sections of 12 uranium isotopes from ENDF/B-Ⅷ.0 in fast neutron region were extracted.The cross sections of 230 U were used as test data to be predicted,the cross sections of 232 U were used as verification data,and the cross sections of other 10 nuclides were used as training data.In order to obtain a neural network model with predictive ability,a series of neural network models were trained by using training data,and then the best model was selected by using verification data to predict test data.In order to find a suitable network structure,neural network models were constructed with a certain step size(10 neurons)within the number of hidden layers 2-10 and the number of neurons in each layer less than 100.After completing all the model training,the best model was selected by using the validation data,the cross section data of test nuclides were predicted by using the best model,and the prediction ability was evaluated by comparing with the data of evaluation library.For the total cross section,taking(A,E)as the features,satisfactory prediction results can be obtained,however,for el

关 键 词：深度神经网络 快中子截面 核数据评价 

分 类 号：O571.55[理学—粒子物理与原子核物理]