  Pulse shape discrimination plays a key role in improving the
signal-to-background ratio in NEOS analysis by removing fast neutrons.
Identifying particles by looking at the tail of the waveform has been an
effective and plausible approach for pulse shape discrimination, but has the
limitation in sorting low energy particles. As a good alternative, the
convolutional neural network can scan the entire waveform as they are to
recognize the characteristics of the pulse and perform shape classification of
NEOS data. This network provides a powerful identification tool for all energy
ranges and helps to search unprecedented phenomena of low-energy, a few MeV or
less, neutrinos.
