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Pulse shape discrimination using a convolutional neural network for organic liquid scintillator signals

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Title
Pulse shape discrimination using a convolutional neural network for organic liquid scintillator signals
Author(s)
Jung, K. Y.; Han, B. Y.; E. J. Jeon; Jeong, Y.; Jo, H. S.; Kim, J. Y.; Kim, J. G.; Y. D. Kim; Y. J. Ko; M. H. Lee; J. Lee; Moon, C. S.; Y. M. Oh; Park, H. K.; S. H. Seo; Seol, D. W.; Siyeon, K.; Sun, G. M.; Yoon, Y. S.; Yu, I.
Publication Date
2023-03
Journal
JOURNAL OF INSTRUMENTATION, v.18, no.3
Publisher
IOP Publishing Ltd
Abstract
A convolutional neural network (CNN) architecture is developed to improve the pulse shape discrimination (PSD) power of the gadolinium-loaded organic liquid scintillation detector to reduce the fast neutron background in the inverse beta decay candidate events of the NEOS-II data. A power spectrum of an event is constructed using a fast Fourier transform of the time domain raw waveforms and put into CNN. An early data set is evaluated by CNN after it is trained using low energy beta and alpha events. The signal-to-background ratio averaged over 1-10 MeV visible energy range is enhanced by more than 20% in the result of the CNN method compared to that of an existing conventional PSD method, and the improvement is even higher in the low energy region.
URI
https://pr.ibs.re.kr/handle/8788114/14249
DOI
10.1088/1748-0221/18/03/P03003
ISSN
1748-0221
Appears in Collections:
Center for Underground Physics(지하실험 연구단) > 1. Journal Papers (저널논문)
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