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Open AccessArticle

Reconstruction of Compton Edges in Plastic Gamma Spectra Using Deep Autoencoder

1
Intelligent Computing Laboratory, Korea Atomic Energy Research Institute, Daejeon 34507, Korea
2
Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
3
Quantum Beam Science Division, Korea Atomic Energy Research Institute, Daejeon 34507, Korea
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(10), 2895; https://doi.org/10.3390/s20102895
Received: 22 April 2020 / Revised: 19 May 2020 / Accepted: 19 May 2020 / Published: 20 May 2020
(This article belongs to the Special Issue Radiation Sensing: Design and Deployment of Sensors and Detectors)
Plastic scintillation detectors are widely utilized in radiation measurement because of their unique characteristics. However, they are generally used for counting applications because of the energy broadening effect and the absence of a photo peak in their spectra. To overcome their weaknesses, many studies on pseudo spectroscopy have been reported, but most of them have not been able to directly identify the energy of incident gamma rays. In this paper, we propose a method to reconstruct Compton edges in plastic gamma spectra using an artificial neural network for direct pseudo gamma spectroscopy. Spectra simulated using MCNP 6.2 software were used to generate training and validation sets. Our model was trained to reconstruct Compton edges in plastic gamma spectra. In addition, we aimed for our model to be capable of reconstructing Compton edges even for spectra having poor counting statistics by designing a dataset generation procedure. Minimum reconstructible counts for single isotopes were evaluated with metric of mean averaged percentage error as 650 for 60Co, 2000 for 137Cs, 3050 for 22Na, and 3750 for 133Ba. The performance of our model was verified using the simulated spectra measured by a PVT detector. Although our model was trained using simulation data only, it successfully reconstructed Compton edges even in measured gamma spectra with poor counting statistics. View Full-Text
Keywords: plastic gamma spectra; energy broadening correction; Compton edge reconstruction; deep learning; deep autoencoder plastic gamma spectra; energy broadening correction; Compton edge reconstruction; deep learning; deep autoencoder
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MDPI and ACS Style

Jeon, B.; Lee, Y.; Moon, M.; Kim, J.; Cho, G. Reconstruction of Compton Edges in Plastic Gamma Spectra Using Deep Autoencoder. Sensors 2020, 20, 2895.

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