Nuclide identification algorithm for Polyvinyl Toluene scintillation detector based on Deep Neural Network

Abstract

Radiation portal monitors (RPMs) are now stationed at strategic areas (airports, ports, etc.) to identify the illegal transportation of radioactive sources and nuclear items. RPMs are typically fitted with a PVT detector with a high recording efficiency. Radioisotope identification from the gamma spectrum acquired on this detector is normally not regarded due to the low resolution. This research describes an artificial neural network-based isotope identification algorithm that was applied to the gamma spectrum collected from the RPM's PVT detector. With excellent precision, this approach can detect one or a mixture of isotopes on the spectrum. The model still recognizes the training isotopes with >89 percent accuracy for spectra with the gain displacement in the range of 20 percent.