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

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 

机构地区：[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

出　　处：《Nuclear Science and Techniques》2023年第3期52-63,共12页核技术（英文）

基　　金：supported by the Hunan Provincial Innovation Foundation for Postgraduates (No.QL20210228);the National Natural Science Foundation of China (No.12075112);the National Natural Science Foundation of China (No.12175102).

摘　　要：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 method was lower than that of the trained convolutional neural network.

关 键 词：Imaging plate Convolutional neural network Alpha tracks counting 

分 类 号：O572.353[理学—粒子物理与原子核物理] TP183[理学—物理]