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Jeon, Eun Ju
지하실험 연구단
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Pulse-shape Discrimination of Fast Neutron Background using Convolutional Neural Network for NEOS II

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Title
Pulse-shape Discrimination of Fast Neutron Background using Convolutional Neural Network for NEOS II
Author(s)
Jeong, Y; Han, BY; Eun Ju Jeon; Jo, HS; Kim, DK; Kim, JY; Kim, JG; Yeongduk Kim; Young Ju Ko; Lee, HM; Moo Hyun Lee; Jaison Lee; Moon, CS; Yoomin Oh; Park, HK; Kang Soon Park; Seon Hee Seo; Siyeon, K; Sun, GM; Yoon, YS; Yu, I
Subject
Reactor antineutrino, ; Inverse beta decay, ; Fast neutron, ; Convolutional neural network, ; Pulse-shape discrimination
Publication Date
2020-12
Journal
JOURNAL OF THE KOREAN PHYSICAL SOCIETY, v.77, no.12, pp.1118 - 1124
Publisher
KOREAN PHYSICAL SOC
Abstract
ⓒ2020 The Korean Physical Society. 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
URI
https://pr.ibs.re.kr/handle/8788114/9434
DOI
10.3938/jkps.77.1118
ISSN
0374-4884
Appears in Collections:
Center for Underground Physics(지하실험 연구단) > 1. Journal Papers (저널논문)
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