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Pulse-shape Discrimination of Fast Neutron Background using Convolutional Neural Network for NEOS II
- Authors
- Jeong, Y.; Han, B. Y.; Jeon, E. J.; Jo, H. S.; Kim, D. K.; Kim, J. Y.; Kim, J. G.; Kim, Y. D.; Ko, Y. J.; Lee, H. M.; Lee, M. H.; Lee, J.; Moon, C. S.; Oh, Y. M.; Park, H. K.; Park, K. S.; Seo, S. H.; Siyeon, K.; Sun, G. M.; Yoon, Y. S.; Yu, I.
- Issue Date
- Dec-2020
- Publisher
- KOREAN PHYSICAL SOC
- Keywords
- Reactor antineutrino; Inverse beta decay; Fast neutron; Convolutional neural network; Pulse-shape discrimination
- Citation
- JOURNAL OF THE KOREAN PHYSICAL SOCIETY, v.77, no.12, pp.1118 - 1124
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- JOURNAL OF THE KOREAN PHYSICAL SOCIETY
- Volume
- 77
- Number
- 12
- Start Page
- 1118
- End Page
- 1124
- ISSN
- 0374-4884
- Abstract
- 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.
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Collections - Graduate School > Department of Accelerator Science > 1. Journal Articles
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