  Convolutional neural networks (CNNs) are widely used state-of-the-art
computer vision tools that are becoming increasingly popular in high energy
physics. In this paper, we attempt to understand the potential of CNNs for
event classification in the NEXT experiment, which will search for neutrinoless
double-beta decay in $^{136}$Xe. To do so, we demonstrate the usage of CNNs for
the identification of electron-positron pair production events, which exhibit a
topology similar to that of a neutrinoless double-beta decay event. These
events were produced in the NEXT-White high-pressure xenon TPC using 2.6-MeV
gamma rays from a $^{228}$Th calibration source. We train a network on Monte
Carlo-simulated events and show that, by applying on-the-fly data augmentation,
the network can be made robust against differences between simulation and data.
The use of CNNs offer significant improvement in signal efficiency/background
rejection when compared to previous non-CNN-based analyses.
