Identifying Potential Exomoon Signals with Convolutional Neural Networks

Targeted observations of possible exomoon host systems will remain difficult to obtain and time-consuming to analyse 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 ∼27 000 examples of synthetic, planet-only, and planet + moon single transits, injected into Kepler light curves. We achieve up to 88 per cent classification accuracy with individual CNN architectures and 97 per cent 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 d (∼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.