A study on Radioisotope Identification Algorithms to enhance characterization capability of Spectroscopic Radiation Portal Monitors in discrimination of Illicit Trafficking of Nuclear Materials from NORMs in goods

Nuclear smuggling and nuclear terrorism are important issues that threaten national and global nuclear security. The smuggling of nuclear and radiological materials and the threats posed by illegal malicious organizations against the use of these materials demonstrate this. In order to prevent or interdict the illicit trafficking of nuclear and radiological materials, standard radiation portal monitors(RPM) are already deployed over 10,000 at the borders, seaports and customs around the world, and also spectroscopic radiation portal monitors (SRPM) are commonly being deployed to enhance the detection capability of them during primary inspection of goods and materials. In recent years, the importance of advancements in SRPMs in combating illicit trafficking of goods and materials and discrimination from NORMs as well as reducing the number of false or nuisance alarms in the workflow is discussed. In this challenging problem, alternative methods or enhancements are studied for RPM/SRPM devices in order to add a radionuclide isotope identification capability. Among them, new emerging algorithms such as conventional methods(peak based Bayesian, NLSQ, Fuzzy Logic etc.) or Artificial or Convolution Neural Networks(ANN/CNN) for radioisotope Identification Algorithms and In this study, importance of radiation portal monitors and a need for spectroscopic analysis feature in portal monitors are discussed. Then, an alternative method to achieve radionuclide isotope identification capability in already deployed standard radiation portal monitors(RPMs) is discussed. Lastly, one of the methods that is used for the radioisotope identification, photopeak based Bayesian Statistics method is discussed.