DART-Vetter: A Deep Learning Tool for Automatic Triage of Exoplanet Candidates

In the identification of new planetary candidates in transit surveys, the employment of deep learning models proved to be essential to efficiently analyze a continuously growing volume of photometric observations. To further improve the robustness of these models, it is necessary to exploit the complementarity of data collected from different transit surveys such as NASA’s Kepler, Transiting Exoplanet Survey Satellite (TESS), and, in the near future, the ESA Planetary Transits and Oscillation of stars mission. In this work, we present a deep learning model, named DART-Vetter, that is able to distinguish planetary candidates from false positives signals detected by any potential transiting survey. DART-Vetter is a convolutional neural network that processes only the light curves folded on the period of the relative signal, featuring a simpler and more compact architecture with respect to other triaging and/or vetting models available in the literature. We trained and tested DART-Vetter on several data sets of publicly available and homogeneously labelled TESS and Kepler light curves in order to prove the effectiveness of our model. Despite its simplicity, DART-Vetter achieves highly competitive triaging performance, with a recall rate of 91% on an ensemble of TESS and Kepler data, when compared to Exominer and Astronet-Triage. Its compact, open source, and easy to replicate architecture makes DART-Vetter a particularly useful tool for automatizing triaging procedures or assisting human vetters, showing a discrete generalization on threshold-crossing events with multiple event statistic > 20 and orbital period < 50 days.