Analyzing Fission Cross Section of U Isotope by Neural Network Method

• Published in: Yuanzineng kexue jishu
• Main Author: TIAN Yuan;XU Ruirui;TAO Xi;WANG Jimin;ZHANG Yue;SUN Xiaodong;ZHANG Zhi;WANG Junchen;XIA Houqiong
• Format: Article
• Language: English
• Published: Editorial Board of Atomic Energy Science and Technology 2023-04-01
• Subjects: fission cross section data; feedforward neural network
• Online Access: https://www.aest.org.cn/CN/10.7538/yzk.2023.youxian.0048

Neutron-induced fission nuclear reaction data are critical for understanding nuclear physics, engineering, and technology. The accuracy of fission cross section of key fuel nuclides and sub-actinide nuclei is increasingly important with the development of new nuclear energy system concepts. Machine learning is a powerful tool for data analysis and modeling, and can be used to extract features from large amounts of data without requiring encoding for a specific task. Neural networks, a subset of machine learning, are particularly effective for mapping input to output for tasks with a sufficient number of target-valued data. In a recent study, researchers applied machine learning methods to analyze fission nuclear reaction cross section data. The team collected fission cross section data for the uranium isotope chain from the experimental nuclear reaction data (EXFOR) library and five sets of frequently used evaluation data from the evaluated nuclear data file (ENDF) library. The neutron-induced fission cross section data of 233-239U in the energy range from 2 to 20 MeV were selected as the training data set. To obtain fission cross sections for a given neutron number, proton number, and corresponding incident neutron energy, the feedforward neural network (FNN) was trained using fission cross section data from experimental measurement of the uranium isotope chain to achieve maximum agreement with experimental and evaluation data. The results show that the fission cross section data generated by the machine learning method can reproduce the step structure of the fission cross section well and is very close to the results of experimental and evaluation data. This provides a research basis for large-scale analysis of fission cross section data of easy-to-fission nuclei. The application of machine learning to nuclear physics and engineering is still in its early stages, and there are several challenges that need to be addressed. One major challenge is the lack of high-quality data sets, which are essential for training and testing machine learning algorithm. In addition, the complexity of nuclear reactions and the need for accurate modeling of the underlying physics present additional challenges. Despite these challenges, machine learning has the potential to revolutionize both the research and practice of nuclear physics and engineering. The ability to analyze and model large amounts of data can help researchers better understand the underlying physics of nuclear reactions and improve the accuracy of predictions. Machine learning algorithm can also be used to optimize the design and operation of nuclear reactors, leading to improved safety and efficiency. In conclusion, machine learning method is a promising approach for analyzing and modeling nuclear physics and engineering data. The application of neural networks to fission cross section data provides a new perspective on the understanding and prediction of nuclear reactions. Continued research and development of machine learning algorithm can contribute to the safe and efficient use of nuclear energy for generations to come.