Pulse shape discrimination using a convolutional neural network for organic liquid scintillator signalsopen access
- Authors
- Jung, K.Y.; Han, B.Y.; Jeon, E.J.; Jeong, Y.; Jo, H.S.; Kim, J.Y.; Kim, J.G.; Kim, Y.D.; Ko, Y.J.; Lee, M.H.; Lee, J.; Moon, C.S.; Oh, Y.M.; Park, H.K.; Seo, S.H.; Seol, D.W.; Kim, Siyeon; Sun, G.M.; Yoon, Y.S.; Yu, I.
- Issue Date
- Mar-2023
- Publisher
- Institute of Physics
- Keywords
- Data Processing; Liquid detectors; Neutrino detectors; Particle identification methods
- Citation
- Journal of Instrumentation, v.18, no.3
- Journal Title
- Journal of Instrumentation
- Volume
- 18
- Number
- 3
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/67320
- DOI
- 10.1088/1748-0221/18/03/P03003
- ISSN
- 1748-0221
- 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 β and α 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. © 2023 IOP Publishing Ltd and Sissa Medialab.
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- Appears in
Collections - College of Natural Sciences > Department of Physics > 1. Journal Articles
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