Pulse shape discrimination using a convolutional neural network for organic liquid scintillator signals. (1st March 2023)

• Record Type: Journal Article
• 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.; Siyeon, K.; Sun, G.M.; Yoon, Y.S.; Yu, I.
• Is Part Of: Journal of instrumentation. Volume 18:Number 3(2023)
• Publication Date: 2023-03-01
• Subjects: Particle identification methods -- Data Processing -- Liquid detectors -- Neutrino detectors; Scientific apparatus and instruments -- Periodicals; 502.84
• DOI: 10.1088/1748-0221/18/03/P03003
• Languages: English
• ISSNs: 1748-0221

Abstract

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.