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NASA's Transiting Exoplanet Survey Satellite (TESS) presents us with anunprecedented volume of space-based photometric observations that must beanalyzed in an efficient and unbiased manner. With at least $\sim1,000,000$ newlight curves generated every month from full frame images alone, automatedplanet candidate identification has become an attractive alternative to humanvetting. Here we present a deep learning model capable of performing triage andvetting on TESS candidates. Our model is modified from an existing neuralnetwork designed to automatically classify Kepler candidates, and is the firstneural network to be trained and tested on real TESS data. In triage mode, ourmodel can distinguish transit-like signals (planet candidates and eclipsingbinaries) from stellar variability and instrumental noise with an averageprecision (the weighted mean of precisions over all classification thresholds)of 97.0% and an accuracy of 97.4%. In vetting mode, the model is trained toidentify only planet candidates with the help of newly added scientific domainknowledge, and achieves an average precision of 69.3% and an accuracy of 97.8%.We apply our model on new data from Sector 6, and present 288 new signals thatreceived the highest scores in triage and vetting and were also identified asplanet candidates by human vetters. We also provide a homogeneously classifiedset of TESS candidates suitable for future training.

Identifying Exoplanets with Deep Learning III: Automated Triage and Vetting of TESS Candidates