  The High-Altitude Water Cherenkov experiment (HAWC) observatory is located
4100 meters above sea level. HAWC is able to detect secondary particles from
extensive air showers (EAS) initiated in the interaction of a primary particle
(either a gamma or a charged cosmic ray) with the upper atmosphere. Because an
overwhelming majority of EAS events are triggered by cosmic rays, background
noise suppression plays an important role in the data analysis process of the
HAWC observatory. Currently, HAWC uses cuts on two parameters (whose values
depend on the spatial distribution and luminosity of an event) to separate
gamma-ray events from background hadronic showers. In this work, a search for
additional gamma-hadron separation parameters was conducted to improve the
efficiency of the HAWC background suppression technique. The best-performing
parameters were integrated to a feed-foward Multilayer Perceptron Neural
Network (MLP-NN), along with the traditional parameters. Various iterations of
MLP-NN's were trained on Monte Carlo data, and tested on Crab data. Preliminary
results show that the addition of new parameters can improve the significance
of the point source at high-energies (~ TeV), at the expense of slightly worse
performance in conventional low-energy bins (~ GeV). Further work is underway
to improve the efficiency of the neural network at low energies.
