  We investigate the potential of using deep learning techniques to reject
background events in searches for neutrinoless double beta decay with high
pressure xenon time projection chambers capable of detailed track
reconstruction. The differences in the topological signatures of background and
signal events can be learned by deep neural networks via training over many
thousands of events. These networks can then be used to classify further events
as signal or background, providing an additional background rejection factor at
an acceptable loss of efficiency. The networks trained in this study performed
better than previous methods developed based on the use of the same topological
signatures by a factor of 1.2 to 1.6, and there is potential for further
improvement.
