Automated triage and vetting of TESS candidates (Yu+, 2019)

Here we present the first Convolutional neural network (CNN) trained and tested on real TESS data. Our model takes as inputs human-labeled light curves produced by the MIT Quick Look Pipeline (QLP; C. Huang et al. 2019, in preparation), and can be trained to perform either triage or vetting on TESS candidates. Like Shallue & Vanderburg (2018AJ....155...94S), we work with possible planet signals, which are called "threshold-crossing events" or TCEs. These are periodic dimming events potentially consistent with signals produced by transiting planets, and are typically identified by an algorithm designed to find such signals. In this study, we adopt the MIT QLP for light-curve production and transit searches.

Identifier
Source http://cdsarc.u-strasbg.fr/cgi-bin/Cat?J/AJ/158/25
Related Identifier 2019AJ....158...25Y
Metadata Access http://dc.g-vo.org/rr/q/pmh/pubreg.xml?verb=GetRecord&metadataPrefix=oai_datacite&identifier=ivo://CDS.VizieR/J/AJ/158/25
Provenance
Creator Yu L., Vanderburg A., Huang C., Shallue C.J., Crossfield I.J.M., Gaudi B.S., Daylan T., Dattilo A., Armstrong D.J., Ricker G.R., Vanderspek R.K., Latham D.W., Seager S., Dittmann J., Doty J.P., Glidden A., Quinn S.N.
Publisher CDS
Publication Year 2019
Rights public
Contact CDS support team <cds-question@unistra.fr>
Representation
Format text/xml+votable
Coverage
Discipline Various