Photometric Search for Exomoons by using Convolutional Neural Networks
This work addresses the search for potentially habitable exomoons, offering a more computationally efficient method compared to classical statistical approaches, though it is incremental as it applies existing deep learning techniques to a new domain.
The paper tackles the problem of detecting exomoons, which are currently unconfirmed beyond our solar system, by using Convolutional Neural Networks (CNNs) trained on synthetic and real light curves, achieving the ability to find moons roughly 2-3 Earth radii in size in datasets like Kepler.
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.