TESS, Transiting Exoplanet Survey Satellite, is a critical mission to increase our understanding of earth-like planets outside our solar system. TESS will survey 200,000 of the brightest stars near us to search for exoplanets; i.e. planets outside our solar system. Given that TESS is surveying a different set of stars every 27 days, manual classification of threshold crossing events (TCEs) is very challenging.

Thus, we need machine classification systems able to automatically determine whether a TCE is a planet candidate (PC) or not. We have learned from our preliminary automatic classification study of Kepler’s TCEs that the features extracted by the pipeline might not be suitable for automatic classification. Therefore, in this work we focus on deep learning techniques that do both feature extraction and classification simultaneously.