Pulse-shape Discrimination of Fast Neutron Background using Convolutional Neural Network for NEOS II

被引：0

作者：

Y. Jeong

B. Y. Han

E. J. Jeon

H. S. Jo

D. K. Kim

J. Y. Kim

J. G. Kim

Y. D. Kim

Y. J. Ko

H. M. Lee

M. H. Lee

J. Lee

C. S. Moon

Y. M. Oh

H. K. Park

K. S. Park

S. H. Seo

K. Siyeon

G. M. Sun

Y. S. Yoon

I. Yu

机构：

[1] Chung-Ang University,Department of Physics

[2] Korea Atomic Energy Research Institute,Neutron Science Division

[3] Institute for Basic Science (IBS),Center for Underground Physics

[4] Kyungpook National University,Department of Physics

[5] Sejong University,Department of Physics and Astronomy

[6] SungKyunKwan University,Department of Physics

[7] University of Science and Technology (UST),IBS School

[8] Korea University,Department of Accelerator Science

[9] Korea Research Institute of Standards and Science,Center for Ionizing Radiation

来源：

Journal of the Korean Physical Society | 2020年 / 77卷

关键词：

Reactor antineutrino; Inverse beta decay; Fast neutron; Convolutional neural network; Pulse-shape discrimination;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Pulse-shape discrimination plays a key role in improving the signal-to-background ratio in NEOS analysis by removing fast neutrons. Identifying particles by looking at the tail of the waveform has been an effective and plausible approach for pulse-shape discrimination, but has the limitation in sorting low energy particles. As a good alternative, the convolutional neural network can scan the entire waveform as they are to recognize the characteristics of the pulse and perform shape classification of NEOS data. This network provides a powerful identification tool for all energy ranges and helps to search unprecedented phenomena of low-energy, a few MeV or less, neutrinos.

引用

页码：1118 / 1124

页数：6

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