闪烁体
探测器
卷积神经网络
中子
卷积(计算机科学)
物理
中子探测
伽马射线
深度学习
信号(编程语言)
人工智能
人工神经网络
计算机科学
模式识别(心理学)
光学
材料科学
作者
Seonkwang Yoon,Chaehun Lee,Byung-Hee Won,Sang-Bum Hong,Hee Seo,Ho-Dong Kim
标识
DOI:10.1007/s40042-022-00398-x
摘要
Due to the high gamma sensitivity of organic scintillators, it is essential to discriminate signals induced by neutron from gamma-ray in fast-neutron detection. With the improvement of digital signal processing techniques, diverse discrimination methods based on pulse-shape variation by radiation type have been developed. The main purpose of this study was to verify the applicability of a deep-learning model, especially convolution neural network (CNN), to pulse-shape discrimination (PSD) in organic scintillation detectors, such as BC-501A (liquid) and EJ-276 (plastic). To that end, waveforms of neutron and gamma-ray were experimentally collected using point sources of 137Cs (gamma-ray) and 252Cf (neutron/gamma-ray) and pre-processed for being compatible with deep-learning. The PSD performance was evaluated for both detectors using the charge comparison method (CCM) which is one of the representative conventional PSD techniques of time-domain. In addition, the CNN-based discriminating algorithms were tested, and its preliminary results were confirmed with confusion matrices which indicate the discrimination accuracy of a deep-learning model.
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