arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2110.01992physics.ins-dethep-exnucl-ex
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基于机器学习和传统方法的Ca(Br,I)$_2$晶体脉冲波形鉴别研究

Comparative pulse shape discrimination study for Ca(Br, I)$_2$ scintillators using machine learning and conventional methods

M. Yoshino, T. Iida, K. Mizukoshi, T. Miyazaki, K. Kamada, K. J. Kim, A. Yoshikawa

摘要:

在粒子物理实验中,脉冲形状鉴别(PSD)是消除信号中主要背景的有力工具。然而,分析方法一直是提高PSD性能的瓶颈。在这项研究中,两种机器学习方法-多层感知器和卷积神经网络-应用于PSD,并将其PSD性能与传统的分析方法进行了比较。用垂直布里奇曼-斯托克巴杰法生长了三种钙基卤化物晶体,并用于评价PSD。与传统的分析方法相比,机器学习方法获得了更好的PSD性能为所有的调制器。对于具有低光输出的照明器,机器学习方法在低能量区域比传统方法更有效地提高PSD精度。

英文摘要:

In particle physics experiments, pulse shape discrimination (PSD) is a powerful tool for eliminating the major background from signals. However, the analysis methods have been a bottleneck to improving PSD performance. In this study, two machine learning methods -- multilayer perceptron and convolutional neural network -- were applied to PSD, and their PSD performance was compared with that of conventional analysis methods. Three calcium-based halide scintillators were grown using the vertical Bridgman--Stockbarger method and used for the evaluation of PSD. Compared with conventional analysis methods, the machine learning methods achieved better PSD performance for all the scintillators. For scintillators with low light output, the machine learning methods were more effective for PSD accuracy than the conventional methods in the low-energy region.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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