  Targeted observations of possible exomoon host systems will remain difficult
to obtain and time-consuming to analyze in the foreseeable future. As such,
time-domain surveys such as Kepler, K2 and TESS will continue to play a
critical role as the first step in identifying candidate exomoon systems, which
may then be followed-up with premier ground- or space-based telescopes. In this
work, we train an ensemble of convolutional neural networks (CNNs) to identify
candidate exomoon signals in single-transit events observed by Kepler. Our
training set consists of ${\sim}$27,000 examples of synthetic, planet-only and
planet+moon single transits, injected into Kepler light curves. We achieve up
to 88\% classification accuracy with individual CNN architectures and 97\%
precision in identifying the moons in the validation set when the CNN ensemble
is in total agreement. We then apply the CNN ensemble to light curves from 1880
Kepler Objects of Interest with periods $>10$ days ($\sim$57,000 individual
transits), and further test the accuracy of the CNN classifier by injecting
planet transits into each light curve, thus quantifying the extent to which
residual stellar activity may result in false positive classifications. We find
a small fraction of these transits contain moon-like signals, though we caution
against strong inferences of the exomoon occurrence rate from this result. We
conclude by discussing some ongoing challenges to utilizing neural networks for
the exomoon search.
