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