Identification of SNM based on low-resolution gamma-ray characteristics and neural network

Abstract

The risk of revealing sensitive information of nuclear weapon is an obstacle for comprehensively applying the identification technology in nuclear verification and nuclear security. In order to reduce the risk, low-resolution radiation spectra are suggested to be used in the activities of identifying special nuclear material (SNM) items’ types. In this article, we proposed an effective algorithm that extracts characteristic information from low-resolution gamma-ray spectra of SNMs and identifies the types of SNMs through backpropagation (BP) neural network and template matching method. We established the algorithm by numerical simulations, and then conducted series of experiments to verify and validate this algorithm. The identification results of applying this algorithm to real plutonium (PU) and high enriched uranium (HEU) pits showed that the proposed algorithm is an eligible option for both nuclear verification and nuclear security.