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The Development of Gamma Energy Identifying Algorithm for Compact Radiation Sensors Using Stepwise Refinement Technique

  • Yoo, Hyunjun (Division of Radiation Regulation, Korea Institute of Nuclear Safety) ;
  • Kim, Yewon (Department of Nuclear & Quantum Engineering, Korea Advanced Institute of Science and Technology) ;
  • Kim, Hyunduk (Department of Nuclear & Quantum Engineering, Korea Advanced Institute of Science and Technology) ;
  • Yi, Yun (Department of Electronics & Information Engineering, Korea University) ;
  • Cho, Gyuseong (Department of Nuclear & Quantum Engineering, Korea Advanced Institute of Science and Technology)
  • Received : 2017.03.21
  • Accepted : 2017.05.11
  • Published : 2017.06.30

Abstract

Background: A gamma energy identifying algorithm using spectral decomposition combined with smoothing method was suggested to confirm the existence of the artificial radio isotopes. The algorithm is composed by original pattern recognition method and smoothing method to enhance the performance to identify gamma energy of radiation sensors that have low energy resolution. Materials and Methods: The gamma energy identifying algorithm for the compact radiation sensor is a three-step of refinement process. Firstly, the magnitude set is calculated by the original spectral decomposition. Secondly, the magnitude of modeling error in the magnitude set is reduced by the smoothing method. Thirdly, the expected gamma energy is finally decided based on the enhanced magnitude set as a result of the spectral decomposition with the smoothing method. The algorithm was optimized for the designed radiation sensor composed of a CsI (Tl) scintillator and a silicon pin diode. Results and Discussion: The two performance parameters used to estimate the algorithm are the accuracy of expected gamma energy and the number of repeated calculations. The original gamma energy was accurately identified with the single energy of gamma radiation by adapting this modeling error reduction method. Also the average error decreased by half with the multi energies of gamma radiation in comparison to the original spectral decomposition. In addition, the number of repeated calculations also decreased by half even in low fluence conditions under $10^4$ ($/0.09cm^2$ of the scintillator surface). Conclusion: Through the development of this algorithm, we have confirmed the possibility of developing a product that can identify artificial radionuclides nearby using inexpensive radiation sensors that are easy to use by the public. Therefore, it can contribute to reduce the anxiety of the public exposure by determining the presence of artificial radionuclides in the vicinity.

Acknowledgement

Grant : Radiation Technical Trouble Shooting Center Management

Supported by : Center for Integrated Smart Sensors, KINS

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