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

Abstract

NASA's Transiting Exoplanet Survey Satellite (TESS) presents us with an unprecedented volume of space-based photometric observations that must be analyzed in an efficient and unbiased manner. With at least $\sim 1,000,000$ new light curves generated every month from full frame images alone, automated planet candidate identification has become an attractive alternative to human vetting. Here we present a deep learning model capable of performing triage and vetting on TESS candidates. Our model is modified from an existing neural network designed to automatically classify Kepler candidates, and is the first neural network to be trained and tested on real TESS data. In triage mode, our model can distinguish transit-like signals (planet candidates and eclipsing binaries) from stellar variability and instrumental noise with an average precision (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 to identify only planet candidates with the help of newly added scientific domain knowledge, 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 that received the highest scores in triage and vetting and were also identified as planet candidates by human vetters. We also provide a homogeneously classified set of TESS candidates suitable for future training.

15 pages, 6 figures, 2 tables, accepted for publication in AJ

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• Nigraha: Machine-learning based pipeline to identify and evaluate planet candidates from TESS
• Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification Framework
• The TESS Triple-9 Catalog II: a new set of 999 uniformly-vetted exoplanet candidates
• Distinguishing a planetary transit from false positives: a Transformer-based classification for planetary transit signals
• DIAmante TESS AutoRegressive Planet Search (DTARPS): I. Analysis of 0.9 Million Light Curves
• The GPU Phase Folding and Deep Learning Method for Detecting Exoplanet Transits
• Searching for Possible Exoplanet Transits from BRITE Data through a Machine Learning Technique
• Identifying Potential Exomoon Signals with Convolutional Neural Networks
• DIAmante TESS AutoRegressive Planet Search (DTARPS): II. Hundreds of New TESS Candidate Exoplanets
• ExoplANNET: A deep learning algorithm to detect and identify planetary signals in radial velocity data
• Exoplanet Transit Candidate Identification in TESS Full-Frame Images via a Transformer-Based Algorithm
• DART-Vetter: A Deep LeARning Tool for automatic triage of exoplanet candidates
• Shallow Transits -- Deep Learning II: Identify Individual Exoplanetary Transits in Red Noise using Deep Learning
• Exploring Late Stellar Evolution in the Era of Large Surveys: Machine Learning Prospects for Hot Subdwarfs and White Dwarfs