Rapid classification of TESS planet candidates with convolutional neural networks

Abstract

Aims. Accurately and rapidly classifying exoplanet candidates from transit surveys is a goal of growing importance as the data rates from space-based survey missions increase. This is especially true for the NASA TESS mission which generates thousands of new candidates each month. Here we created the first deep-learning model capable of classifying TESS planet candidates. Methods. We adapted an existing neural network model and then trained and tested this updated model on four sectors of high-fidelity, pixel-level TESS simulations data created using the Lilith simulator and processed using the full TESS pipeline. With the caveat that direct transfer of the model to real data will not perform as accurately, we also applied this model to four sectors of TESS candidates. Results. We find our model performs very well on our simulated data, with 97% average precision and 92% accuracy on planets in the two-class model. This accuracy is also boosted by another ~4% if planets found at the wrong periods are included. We also performed three-class and four-class classification of planets, blended and target eclipsing binaries, and non-astrophysical false positives, which have slightly lower average precision and planet accuracies but are useful for follow-up decisions. When applied to real TESS data, 61% of threshold crossing events (TCEs) coincident with currently published TESS objects of interest are recovered as planets, 4% more are suggested to be eclipsing binaries, and we propose a further 200 TCEs as planet candidates.