  Nuclear reactors produce a high flux of MeV-scale antineutrinos that can be
observed through inverse beta-decay (IBD) interactions in particle detectors.
Reliable detection of reactor IBD signals depends on suppression of
backgrounds, both by physical shielding and vetoing and by pattern recognition
and rejection in acquired data. A particularly challenging background to
reactor antineutrino detection is from cosmogenically induced fast neutrons,
which can mimic the characteristics of an IBD signal. In this work, we explore
two methods of machine learning -- a tree-based classifier and a
graph-convolutional neural network -- to improve rejection of fast
neutron-induced background events in a water Cherenkov detector. The tree-based
classifier examines classification at the reconstructed feature level, while
the graphical network classifies events using only the raw signal data. Both
methods improve the sensitivity for a background-dominant search over
traditional cut-and-count methods, with the greatest improvement being from the
tree-based classification method. These performance enhancements are relevant
for reactor monitoring applications that make use of deep underground oil-based
or water-based kiloton-scale detectors with multichannel, PMT-based readouts,
and they are likely extensible to other similar physics analyses using this
class of detector.
