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- W4322733257 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." @default.
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- W4322733257 date "2023-03-01" @default.
- W4322733257 modified "2023-09-30" @default.
- W4322733257 title "Pulse shape discrimination using a convolutional neural network for organic liquid scintillator signals" @default.
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- W4322733257 doi "https://doi.org/10.1088/1748-0221/18/03/p03003" @default.
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