Many moons have been detected around planets in our Solar System, but none
has been detected unambiguously around any of the confirmed extrasolar planets.
We test the feasibility of a supervised convolutional neural network to
classify photometric transit light curves of planet-host stars and identify
exomoon transits, while avoiding false positives caused by stellar variability
or instrumental noise. Convolutional neural networks are known to have
contributed to improving the accuracy of classification tasks. The network
optimization is typically performed without studying the effect of noise on the
training process. Here we design and optimize a 1D convolutional neural network
to classify photometric transit light curves. We regularize the network by the
total variation loss in order to remove unwanted variations in the data
features. Using numerical experiments, we demonstrate the benefits of our
network, which produces results comparable to or better than the standard
network solutions. Most importantly, our network clearly outperforms a
classical method used in exoplanet science to identify moon-like signals. Thus
the proposed network is a promising approach for analyzing real transit light
curves in the future
