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Unfolding Plastic Detector Responses to White and Mono-energetic Neutrons Based on Artificial Neural Networks

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Abstract

Neutron spectrum unfolding based on an artificial neural network (ANN) is a highly flexible and robust method that can be applied to any type of radiation detector and wide energy range of incident particles. In this study, we present details of neutron-spectrum deconvolution with well-established multilayer perceptron algorithms implemented in CERN ROOT with the aim of obtaining the incident neutron energy spectra for white neutron and mono-energetic beams with finite energy spreads. The ANN trained with experimental and simulation datasets successfully approximated incident neutron spectra with high accuracy for each case, indicating that a well-trained ANN has high potential for applications in radiation-related fields.

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Acknowledgments

This work has been supported through KOMAC (Korea Multi-purpose Accelerator Complex) operation fund of KAERI and the National Research Foundation of Korea (NRF) grant (No. NRF-2017M2A2A6A02071070 and NRF-2018M2A2B3A02072238) funded by the Ministry of Science and ICT (MSIT).

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Authors and Affiliations

  1. Korea Multi-purpose Accelerator Complex, Korea Atomic Energy Research Institute, Gyeonju, 38180, Korea

    Pilsoo Lee, Jeong-Jeung Dang, Han-Sung Kim & Hyeok-Jung Kwon

Authors
  1. Pilsoo Lee
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  2. Jeong-Jeung Dang
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  3. Han-Sung Kim
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  4. Hyeok-Jung Kwon
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Correspondence to Pilsoo Lee.

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Lee, P., Dang, JJ., Kim, HS. et al. Unfolding Plastic Detector Responses to White and Mono-energetic Neutrons Based on Artificial Neural Networks. J. Korean Phys. Soc. 75, 878–881 (2019). https://doi.org/10.3938/jkps.75.878

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  • DOI: https://doi.org/10.3938/jkps.75.878

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