Research and Development of Source Term Activity Reconstruction System Based on Deep Learning

Abstract

The source term is hard to be located and measured because it often distributes in or inside objectives. In practice, the hot spot and source intensity are measured by the gamma camera then the radiation field is estimated by particle transport algorithm as a reference in scheme planning. Hence, this paper presents a source term activity reconstruction (hereinafter referred to as STAR) method based on deep learning to solve this problem. Firstly, a UNET liked framework is constructed to establish the correlation between the radiation field and source activity. Secondly, a source activity reconstruction problem is used to validate the framework. Then we deploy it to the Raspberry Pi with a γ-ray detector. The results show that the average reconstruction error is less than 15% . Therefore, it can be widely used in nuclear facility decommissioning to improve the efficiency of source reconstruction.

Keywords: Source term reconstruction, Deep Learning, Nuclear facility decommissioning, γ-ray dose detector, Raspberry Pi