Pulse-shape Discrimination of Fast Neutron Background using Convolutional Neural Network for NEOS II
| Autor: | Intae Yu, GM Sun, Do-Eok Kim, Kang Soon Park, Heung-Youp Lee, Jy Kim, Moo Hyun Lee, CS Moon, Bo-Young Han, YS Yoon, Seon Hee Seo, Yoomin Oh, Yeongduk Kim, HK Park, Jwajin Kim, Jongmin Lee, Kim Siyeon, Eun Ju Jeon, Y Jeong, Y. J. Ko, HS Jo |
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| Rok vydání: | 2020 |
| Předmět: |
010302 applied physics
Computer science business.industry Sorting General Physics and Astronomy Pattern recognition 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Convolutional neural network Neutron temperature Pulse (physics) Identification (information) 0103 physical sciences Waveform Neutron Artificial intelligence 0210 nano-technology business Energy (signal processing) |
| Zdroj: | Journal of the Korean Physical Society. 77:1118-1124 |
| ISSN: | 1976-8524 0374-4884 |
| DOI: | 10.3938/jkps.77.1118 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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