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.

Keywords:

‎Energy windowing , ‎Naturally occurring radioactive , ‎Plastic scintillation detector , ‎Radiation monitoring ports , ‎Gamma ray detection , ‎Radiation monitoring ports‎