Neural network based radioisotope discrimination on polyvinyl toluene radiation portal monitors

Radiation portal monitors (RPMs) are an effective mean of detecting radioactive material inside cargo containers. Polyvinyl toluene (PVT) monitors are the most broadly extended mainly due to their cost. The drawback when compared to other detectors is the lower resolution of the measured energy spectra. This low resolution hinders the use of spectrometric analysis to discriminate isotopes and discard nuisance alarms. Every alarm must thus be checked in a second inspection by a handheld detector or a spectroscopy-based radiation portal. The cost of this secondary inspection in terms of throughput can be significant, specially at maritime ports and borders. This paper aim is to assess the ability of neural networks to discriminate radioactive isotopes from the energy spectrum as measured by PVT RPMs. For this purpose, the system proposed preprocesses these energy spectra, dividing them by specific zones and transforming them into information. In a second step, this information is used by the neural network architecture, which allows to classify the radioisotopes in different groups.