  Until now, there is no confirmed moon beyond our solar system (exomoon).
Exomoons offer us new possibly habitable places which might also be outside the
classical habitable zone. But until now, the search for exomoons needs much
computational power because classical statistical methods are employed. It is
shown that exomoon signatures can be found by using deep learning and
Convolutional Neural Networks (CNNs), respectively, trained with synthetic
light curves combined with real light curves with no transits. It is found that
CNNs trained by combined synthetic and observed light curves may be used to
find moons bigger or equal to roughly 2-3 earth radii in the Kepler data set or
comparable data sets. Using neural networks in future missions like Planetary
Transits and Oscillation of stars (PLATO) might enable the detection of
exomoons.
