Pulse shape discrimination using a convolutional neural network for organic liquid scintillator signals
Kyudan Jung, B.-Y. Han, E. J. Jeon, Yuna Jeong, H. S. Jo, J.Y. Kim, Jinyeol Kim, Yun-Heung Kim, Y. J. Ko, M.H. Lee, J. Lee, C. S. Moon, Yoomin Oh, H. Park, Sungbal Seo, Donghyeon Seol, K. Siyeon, Gwang‐Min Sun, Y. S. Yoon, I. Yu
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.