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.
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