Research on inversion method for complex source-term distributions based on deep neural networks

• Published in: Nuclear science and techniques 2023-12, Vol.34 (12), p.159-176, Article 195
• Main Authors: Hao, Yi-Sheng, Wu, Zhen, Pu, Yan-Heng, Qiu, Rui, Zhang, Hui, Li, Jun-Li
• Format: Article
• Language: English
• Subjects: Beam Physics; Nuclear Energy; Particle Acceleration and Detection; Particle and Nuclear Physics; Physics; Physics and Astronomy
• ISSN: 1001-8042; 2210-3147
• DOI: 10.1007/s41365-023-01327-8

This study proposes a source distribution inversion convolutional neural network (SDICNN), which is deep neural network model for the inversion of complex source distributions, to solve inversion problems involving fixed-source distributions. A function is developed to obtain the distribution information of complex source terms from radiation parameters at individual sampling points in space. The SDICNN comprises two components: a fully connected network and a convolutional neural network. The fully connected network mainly extracts the parameter measurement information from the sampling points, whereas the convolutional neural network mainly completes the fine inversion of the source-term distribution. Finally, the SDICNN obtains a high-resolution source-term distribution image. In this study, the proposed source-term inversion method is evaluated based on typical geometric scenarios. The results show that, unlike the conventional fully connected neural network, the SDICNN model can extract the two-dimensional distribution features of the source terms, and its inversion results are better. In addition, the effects of the shielding mechanism and number of sampling points on the inversion process are examined. In summary, the result of this study can facilitate the accurate assessment of dose distributions in nuclear facilities.