Identifying Exoplanets with Deep Learning. III. Automated Triage and Vetting of TESS Candidates
View/ Open
Issue Date
2019-06-18Author
Yu, Liang
Vanderburg, Andrew
Huang, Chelsea
Shallue, Christopher J.
Crossfield, Ian
Gaudi, B. Scott
Daylan, Tansu
Dattilo, Anne
Armstrong, David J.
Ricker, George R.
Vanderspek, Roland K.
Latham, David W.
Seager, Sara
Dittmann, Jason
Doty, John P.
Glidden, Ana
Quinn, Samuel N.
Publisher
IOP Publishing
Type
Article
Article Version
Scholarly/refereed, publisher version
Rights
© 2019. The American Astronomical Society. All rights reserved.
Metadata
Show full item recordAbstract
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 ~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.
Collections
Citation
Liang Yu et al 2019 AJ 158 25
Items in KU ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
We want to hear from you! Please share your stories about how Open Access to this item benefits YOU.