Exoplanet detection with Genesis

Abstract

Convolutional Neural Networks (CNNs) have shown to offer a consistent and reliable foundation for the automatic detection of potential exoplanets. CNNs rely on an abundance of parameters (overparameterization) to achieve their impressive detection performances. Astronet was one of the first CNNs for exoplanet detection. It takes as input folded lightcurves in two views: a local view (the transit) and a global view (the entire orbital period including the transit). A more recent CNN called Exonet-XS improved on Astronet’s performance while having considerably less parameters, thereby reducing the risk of overfitting. Exonet-XS also uses two views as input. In this paper, we propose Genesis, an even more simplified CNN for exoplanet detection from folded lightcurves using only one view. In addition, we propose to use a more reliable validation procedure that is custom in CNN-based exoplanet detection studies: the Monte Carlo Cross-Validation (MCCV) procedure. We show that the use of MCCV improves the reliability of the estimation of the detection performance by providing a (discretized) probability distribution, rather than a point estimate. Using MCCV we show that Astronet with only one view performs on a par with the original two-view version. More importantly, our fair comparative evaluation (without stellar parameters and centroids) reveals that Genesis outperforms Exonet-XS and Astronet. We conclude by stating that existing exoplanet detection CNNs are too complex for the task at hand and that future evaluations of performances should use MCCV or similar validation procedures.