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 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.
Source: Astronomy and Astrophysics
Authors: H. P. Osborn, M. Ansdell, Y. Ioannou, M. Sasdelli, D. Angerhausen, D. Caldwell, J. M. Jenkins, C. Räissi, J. C. Smith
Institutions: Centre National de la Recherche Scientifique, Aix-Marseille Université, Laboratoire d’Astrophysique de Marseille, University of California, Berkeley, University of Cambridge, The University of Adelaide, University of Bern, Blue Marble Space Institute of Science, Center for Space and Habitability, Ames Research Center, Search for Extraterrestrial Intelligence, Institut national de recherche en sciences et technologies du numérique
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $1,680
Awards: G1504207432, WHAT ARE THE PROPERTIES EXOPLANETARY SYSTEMS, PARTICULARLY AS THEY RELATE TO THEIR FORMATION, EVOLUTION, AND POTENTIAL TO MOTIVATE SETI PROGRAMS? OU, CHARTING THE CO-EVOLUTION OF EXOPLANETS AND DEBRIS DISKS. HOW DO YOU EXPLAIN THE EXISTENCE OF GAS GIANT PLANETS TENS OF AU FROM THE HOST STAR WHERE M, G751609732, G7786096128, G81539058, Direct detection and characterization of exoplanetary systems
OpenAlex ID: W2917641111