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TESS data for asteroseismology (T’DA) stellar variability classification pipeline : setup and application to the Kepler Q9 data

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Audenaert, J., Kuszlewicz, J. S., Handberg, R., Tkachenko, A., Armstrong, David J., Hon, M., Kgoadi, R., Lund, M. N., Bell, K. J., Bugnet, L., Bowman, D. M., Johnston, C., García, R. A., Stello, D., Molnár, L., Plachy, E., Buzasi, D. and Aerts, C. (2021) TESS data for asteroseismology (T’DA) stellar variability classification pipeline : setup and application to the Kepler Q9 data. The Astronomical Journal, 162 (5). 209. doi:10.3847/1538-3881/ac166a

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Official URL: https://doi.org/10.3847/1538-3881/ac166a

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Abstract

The NASA Transiting Exoplanet Survey Satellite (TESS) is observing tens of millions of stars with time spans ranging from ∼27 days to about 1 yr of continuous observations. This vast amount of data contains a wealth of information for variability, exoplanet, and stellar astrophysics studies but requires a number of processing steps before it can be fully utilized. In order to efficiently process all the TESS data and make it available to the wider scientific community, the TESS Data for Asteroseismology working group, as part of the TESS Asteroseismic Science Consortium, has created an automated open-source processing pipeline to produce light curves corrected for systematics from the short- and long-cadence raw photometry data and to classify these according to stellar variability type. We will process all stars down to a TESS magnitude of 15. This paper is the next in a series detailing how the pipeline works. Here, we present our methodology for the automatic variability classification of TESS photometry using an ensemble of supervised learners that are combined into a metaclassifier. We successfully validate our method using a carefully constructed labeled sample of Kepler Q9 light curves with a 27.4 days time span mimicking single-sector TESS observations, on which we obtain an overall accuracy of 94.9%. We demonstrate that our methodology can successfully classify stars outside of our labeled sample by applying it to all ∼167,000 stars observed in Q9 of the Kepler space mission.

Item Type: Journal Article
Subjects: Q Science > QB Astronomy
Divisions: Faculty of Science > Physics
SWORD Depositor: Library Publications Router
Library of Congress Subject Headings (LCSH): Astroseismology, Extrasolar planets, Machine learning
Journal or Publication Title: The Astronomical Journal
Publisher: American Astronomical Society
ISSN: 1538-3881
Official Date: 21 October 2021
Dates:
DateEvent
21 October 2021Published
12 July 2021Accepted
Volume: 162
Number: 5
Article Number: 209
DOI: 10.3847/1538-3881/ac166a
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Copyright Holders: © 2021. The American Astronomical Society. All rights reserved.
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
670519:MAMSIE[ERC] Horizon 2020 Framework Programmehttp://dx.doi.org/10.13039/100010661
C16/18/005: PARADISEOnderzoeksraad, KU Leuvenhttp://dx.doi.org/10.13039/501100004497
G0H5416NFonds Wetenschappelijk Onderzoekhttp://dx.doi.org/10.13039/501100003130
ST/R00384X/1[STFC] Science and Technology Facilities Councilhttp://dx.doi.org/10.13039/501100000271
DNRF106Danmarks Grundforskningsfondhttp://dx.doi.org/10.13039/501100001732
80NSSC18K1585[NASA] National Aeronautics and Space Administrationhttp://dx.doi.org/10.13039/100000104
80NSSC19K0379[NASA] National Aeronautics and Space Administrationhttp://dx.doi.org/10.13039/100000104
AST-1903828National Science Foundationhttp://dx.doi.org/10.13039/501100008982
FP7/2007-2013Seventh Framework Programmehttp://dx.doi.org/10.13039/100011102
338251European Research Councilhttp://dx.doi.org/10.13039/501100000781
1286521NFonds Wetenschappelijk Onderzoekhttp://dx.doi.org/10.13039/501100003130
G0A2917NFonds Wetenschappelijk Onderzoekhttp://dx.doi.org/10.13039/501100003130
KH 18 130405Nemzeti Kutatási és Technológiai Hivatalhttp://dx.doi.org/10.13039/501100003827
LP2014-17Magyar Tudományos Akadémiahttp://dx.doi.org/10.13039/501100003825
LP2018-7/2020Magyar Tudományos Akadémiahttp://dx.doi.org/10.13039/501100003825
80NSSC19K0385[NASA] National Aeronautics and Space Administrationhttp://dx.doi.org/10.13039/100000104
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