Counting of alpha particle tracks on imaging plate based on a convolutional neural network

Feng-Di Qin; Han-Yu Luo; Zheng-Zhong He; Ke-Jun Lu; Chuan-Gao Wang; Meng-Meng Wu; Zhong-Kai Fan; Jian Shan

doi:10.1007/s41365-023-01190-7

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Counting of alpha particle tracks on imaging plate based on a convolutional neural network

NUCLEAR PHYSICS AND INTERDISCIPLINARY RESEARCH | Updated：2023-04-20

- Counting of alpha particle tracks on imaging plate based on a convolutional neural network
- Nuclear Science and Techniques Vol. 34, Issue 3, Article number: 37(2023)
- Affiliations：
  
  1.School of Nuclear Science and Technology, Hunan provincial key laboratory of radon, University of South China, Hengyang 421001, China
  2.School of Computer Science, University of South China, Hengyang 421001, China
  3.China Institute of Atomic Energy, Beijing 102413, China
- Author bio：
  
  * shanjian0666@163.com.
- Funds：
- DOI：10.1007/s41365-023-01190-7
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Feng-Di Qin, Han-Yu Luo, Zheng-Zhong He, et al. Counting of alpha particle tracks on imaging plate based on a convolutional neural network. [J]. Nuclear Science and Techniques 34(3):37(2023)
DOI：

Feng-Di Qin, Han-Yu Luo, Zheng-Zhong He, et al. Counting of alpha particle tracks on imaging plate based on a convolutional neural network. [J]. Nuclear Science and Techniques 34(3):37(2023) DOI： 10.1007/s41365-023-01190-7.

摘要

Abstract

Imaging plates are widely used to detect alpha particles to track information, and the number of alpha particle tracks is affected by the overlapping and fading effects of the track information. In this study, an experiment and a simulation were used to calibrate the efficiency parameter of an imaging plate, which was used to calculate the grayscale. Images were created by using grayscale, which trained the convolutional neural network to count the alpha tracks. The results demonstrated that the trained convolutional neural network can evaluate the alpha track counts based on the source and background images with a wider linear range, which was unaffected by the overlapping effect. The alpha track counts were unaffected by the fading effect within 60 min, where the calibrated formula for the fading effect was analyzed for 132.7 min. The detection efficiency of the trained convolutional neural network for inhomogeneous ,241,Am sources (2π emission) was 0.6050 ± 0.0399, whereas the efficiency curve of the photo-stimulated luminescence (PSL) method was lower than that of the trained convolutional neural network.

关键词

Keywords

Imaging plateConvolutional neural networkAlpha tracks counting

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