  Nowadays the implementation of artificial neural networks in high-energy
physics has obtained excellent results on improving signal detection. In this
work we propose to use neural networks (NNs) for event discrimination in HAWC.
This observatory is a water Cherenkov gamma-ray detector that in recent years
has implemented algorithms to identify horizontal muon tracks. However, these
algorithms are not very efficient. In this work we describe the implementation
of three NNs: two based on image classification and one based on object
detection. Using these algorithms we obtain an increase in the number of
identified tracks. The results of this study could be used in the future to
improve the performance of the Earth-skimming technique for the indirect
measurement of neutrinos with HAWC.
