Radiation Physics and Engineering
A peer-reviewed journal published by K. N. Toosi University of Technology

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

Document Type : Research Article

Authors

‎Reza Ziyaee Sisakht
‎Fereydoun Abbasi Davani‎
‎Rouhollah Ghaderi

Faculty of Nuclear Engineering‎, ‎Shahid Beheshti University‎, ‎Tehran‎, ‎Iran

https://doi.org/10.22034/rpe.2020.63478
Abstract
‎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.

Highlights

  • Energy windowing is used to distinguish naturally occurring from unauthorized radioactive.
  • High enriched and depleted uranium, Pu-239, and Cs-137 are used as unauthorized radioactives.
  • Energy windows are obtained for four unauthorized radioactive materials.
  • An artificial neural network is used for processing the energy windowing outputs.

Keywords

‎Energy windowing
‎Naturally occurring radioactive
‎Plastic scintillation detector
‎Radiation monitoring ports
‎Gamma ray detection
‎Radiation monitoring ports‎
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Radiation Physics and Engineering
Volume 1, Issue 2
Spring 2020
Pages 23-26

  • Receive Date 26 September 2017
  • Revise Date 17 November 2017
  • Accept Date 12 February 2018

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