Nuclide identification algorithm for Polyvinyl Toluene scintillation detector based on Deep Neural Network - Cao , Van Hiep; Dinh , Tien Hung; Cao , Dang Luu; Le , Manh Duc; Pham , Dinh Khang (Nuclear Science and Technology, 2026)
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 plastic scintillations detectors with high detection efficiency. Radioisotope identification from the gamma spectrum acquired on this detector usually is not regarded due to the low resolution. This research describes an artificial neural network-based isotope identification algorithm applied to the gamma spectrum collected from the RPM's PVT detector. Measured and simulated gamma spectra of 5 radionuclides are used for training and validating the proposed model, namely 241Am, 133Ba, 137Cs, 60Co, and 152Eu. The recognition accuracy of the proposed model with these radionuclides are 99.0%, 98.0%, 99.0%, 97.5%, and 98.5%, respectively. The model still recognizes the training isotopes with the lowest accuracy of 89.0% for spectra with the displacement in the range of 20%.