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

Nuclear Science and Techniques 2023年第3期34卷 52-63页

作者： Feng-Di Qin Han-Yu Luo Zheng-Zhong He Ke-Jun Lu Chuan-Gao Wang Meng-Meng Wu Zhong-Kai Fan Jian Shan School of Nuclear Science and Technology Hunan Provincial Key Laboratory of RadonUniversity of South ChinaHengyang 421001China School of Computer Science University of South ChinaHengyang 421001China China Institute of Atomic Energy Beijing 102413China

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 *** this study,an experiment and a simulation were used to calibrate the efficiency parameter of an imaging plate,which was used to calculate the *** were created by using grayscale,which trained the convolutional neural network to count the alpha *** 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 *** 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 *** 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 method was lower than that of the trained convolutional neural network.

关键词： Imaging plate Convolutional neural network alpha tracks counting

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