Rapid classification of TESS planet candidates with convolutional neural networks
Work
Year: 2019
Type: article
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 ... more
Source: Astronomy and Astrophysics
Institutions Aix-Marseille Université, Centre National de la Recherche Scientifique, Laboratoire d’Astrophysique de Marseille, Planetary Science Institute, University of Cambridge +8 more
Cites: 53
Cited by: 18
Related to: 10
FWCI: 3.199
Citation percentile (by year/subfield): 87
Topic: Astro and Planetary Science
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Funders National Science Foundation, National Aeronautics and Space Administration, National Aeronautics and Space Administration, Centre National d’Etudes Spatiales
Grant IDS AST-1518332, NNX15AD95G/NEXSS, NNX15AC89G, 131425-PLATO