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Accurately and rapidly classifying exoplanet candidates from transit surveysis a goal of growing importance as the data rates from space-based surveymissions increases. This is especially true for NASA's TESS mission whichgenerates thousands of new candidates each month. Here we created the firstdeep learning model capable of classifying TESS planet candidates. We adaptedthe neural network model of Ansdell et al. (2018) to TESS data. We then trainedand tested this updated model on 4 sectors of high-fidelity, pixel-levelsimulations data created using the Lilith simulator and processed using thefull TESS SPOC pipeline. We find our model performs very well on our simulateddata, with 97% average precision and 92% accuracy on planets in the 2-classmodel. This accuracy is also boosted by another ~4% if planets found at thewrong periods are included. We also performed 3- and 4-class classification ofplanets, blended & target eclipsing binaries, and non-astrophysical falsepositives, which have slightly lower average precision and planet accuracies,but are useful for follow-up decisions. When applied to real TESS data, 61% ofTCEs coincident with currently published TOIs are recovered as planets, 4% moreare suggested to be EBs, and we propose a further 200 TCEs as planetcandidates.

Rapid Classification of TESS Planet Candidates with Convolutional Neural Networks