The use of artificial neural networks to distinguish naturally occurring radioactive materials from unauthorized radioactive materials using a plastic scintillation detector

Distinguishing naturally occurring radioactive (e.g. ceramics, fertilizers, etc.) from unauthorized materials (e.g. high enriched uranium, Pu-239, etc.) to reduce false alarms is a prominent characteristic of radiation monitoring port. By employing the energy windowing method for the spectrum correspond to the simulation of a plastic scintillator detector using the MCNPX Monte Carlo code together with an artificial neural network, the present work proposes a method for distinguishing naturally occurring materials and K-40 from four unauthorized sources including high enriched uranium and Pu-239 (as special nuclear materials), Cs-137 (as an example of dirty bombs), and depleted uranium.