{"date_created":"2022-07-18T11:54:55Z","month":"10","citation":{"mla":"Audenaert, J., et al. “TESS Data for Asteroseismology (T’DA) Stellar Variability Classification Pipeline: Setup and Application to the Kepler Q9 Data.” <i>The Astronomical Journal</i>, vol. 162, no. 5, 209, IOP Publishing, 2021, doi:<a href=\"https://doi.org/10.3847/1538-3881/ac166a\">10.3847/1538-3881/ac166a</a>.","ama":"Audenaert J, Kuszlewicz JS, Handberg R, et al. TESS Data for Asteroseismology (T’DA) stellar variability classification pipeline: Setup and application to the Kepler Q9 data. <i>The Astronomical Journal</i>. 2021;162(5). doi:<a href=\"https://doi.org/10.3847/1538-3881/ac166a\">10.3847/1538-3881/ac166a</a>","short":"J. Audenaert, J.S. Kuszlewicz, R. Handberg, A. Tkachenko, D.J. Armstrong, M. Hon, R. Kgoadi, M.N. Lund, K.J. Bell, L.A. Bugnet, D.M. Bowman, C. Johnston, R.A. García, D. Stello, L. Molnár, E. Plachy, D. Buzasi, C. Aerts, The Astronomical Journal 162 (2021).","ieee":"J. Audenaert <i>et al.</i>, “TESS Data for Asteroseismology (T’DA) stellar variability classification pipeline: Setup and application to the Kepler Q9 data,” <i>The Astronomical Journal</i>, vol. 162, no. 5. IOP Publishing, 2021.","ista":"Audenaert J, Kuszlewicz JS, Handberg R, Tkachenko A, Armstrong DJ, Hon M, Kgoadi R, Lund MN, Bell KJ, Bugnet LA, Bowman DM, Johnston C, García RA, Stello D, Molnár L, Plachy E, Buzasi D, 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.","chicago":"Audenaert, J., J. S. Kuszlewicz, R. Handberg, A. Tkachenko, D. J. Armstrong, M. Hon, R. Kgoadi, et al. “TESS Data for Asteroseismology (T’DA) Stellar Variability Classification Pipeline: Setup and Application to the Kepler Q9 Data.” <i>The Astronomical Journal</i>. IOP Publishing, 2021. <a href=\"https://doi.org/10.3847/1538-3881/ac166a\">https://doi.org/10.3847/1538-3881/ac166a</a>.","apa":"Audenaert, J., Kuszlewicz, J. S., Handberg, R., Tkachenko, A., Armstrong, D. J., Hon, M., … Aerts, C. (2021). TESS Data for Asteroseismology (T’DA) stellar variability classification pipeline: Setup and application to the Kepler Q9 data. <i>The Astronomical Journal</i>. IOP Publishing. <a href=\"https://doi.org/10.3847/1538-3881/ac166a\">https://doi.org/10.3847/1538-3881/ac166a</a>"},"article_processing_charge":"No","acknowledgement":"The research leading to these results has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 670519: MAMSIE), from the KU Leuven Research Council (grant C16/18/005: PARADISE), from the Research Foundation Flanders (FWO) under grant agreement G0H5416N (ERC Runner Up Project), as well as from the BELgian federal Science Policy Office (BELSPO) through PRODEX grant PLATO. D.J.A acknowledges support from the STFC via an Ernest Rutherford Fellowship (ST/R00384X/1). Funding for the Stellar Astrophysics Centre is provided by The Danish National Research Foundation (grant agreement No.: DNRF106). R.H. and M.N.L. acknowledge the ESA PRODEX program. This research was supported by the National Aeronautics and Space Administration (80NSSC18K1585 and 80NSSC19K0379) awarded through the TESS Guest Investigator Program. K.J.B. is supported by the National Science Foundation under Award AST-1903828. J.S.K and K.J.B. were supported by funding from the European Research Council under the European Community's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. 338251 (StellarAges). D.M.B. gratefully acknowledges funding from a senior postdoctoral fellowship from the Research Foundation Flanders (FWO) with grant agreement No. 1286521N. The research leading to these results has received funding from the Research Foundation Flanders (FWO) under grant agreement G0A2917N (BlackGEM). R.A.G. acknowledges support from the GOLF and PLATO CNES grants. L.M. was supported by the Premium Postdoctoral Research Program of the Hungarian Academy of Sciences. The research leading to these results has been supported by the Hungarian National Research, Development, and Innovation Office (NKFIH) grant KH_18 130405 and the Lendület LP2014-17 and LP2018-7/2020 grants of the Hungarian Academy of Sciences. D.B. acknowledges support from the NASA TESS Guest Investigator Program under award 80NSSC19K0385.\r\n\r\nThis paper includes data collected by the TESS mission, which are publicly available from the Mikulski Archive for Space Telescopes (MAST). Funding for the TESS mission is provided by NASA's Science Mission directorate. This research has made use of NASA's Astrophysics Data System as well as the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. Funding for the TESS Asteroseismic Science Operations Centre is provided by the Danish National Research Foundation (Grant agreement no.: DNRF106), ESA PRODEX (PEA 4000119301), and the Stellar Astrophysics Centre (SAC) at Aarhus University. We thank the TESS team and staff and TASC/TASOC for their support of the present work.\r\n\r\nThis paper includes data collected by the Kepler mission. Funding for the Kepler and K2 mission was provided by NASA's Science Mission Directorate. The authors acknowledge the efforts of the Kepler Mission team in obtaining the light-curve data and data validation products used in this publication. These data were generated by the Kepler Mission science pipeline through the efforts of the Kepler Science Operations Center and Science Office. The Kepler light curves are archived at the Mikulski Archive for Space Telescopes.\r\n\r\nThe numerical results presented in this work were obtained at the Centre for Scientific Computing, Aarhus. 37 This research made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013, 2018).\r\n\r\nSoftware: Scikit-learn (Pedregosa et al. 2011), Numpy (Harris et al. 2020), Astropy (Astropy Collaboration et al. 2013, 2018), Scipy (Virtanen et al. 2020), Pandas (McKinney 2010; Pandas Development Team 2020), Lightkurve (Lightkurve Collaboration et al. 2018), XGBoost (Chen & Guestrin 2016), Tensorflow (Abadi et al. 2015).","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","external_id":{"arxiv":["2107.06301"]},"status":"public","publication_identifier":{"eissn":["1538-3881"],"issn":["0004-6256"]},"type":"journal_article","extern":"1","volume":162,"date_updated":"2022-08-19T10:01:56Z","article_number":"209","abstract":[{"lang":"eng","text":"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."}],"publication_status":"published","publication":"The Astronomical Journal","doi":"10.3847/1538-3881/ac166a","author":[{"last_name":"Audenaert","first_name":"J.","full_name":"Audenaert, J."},{"last_name":"Kuszlewicz","first_name":"J. S.","full_name":"Kuszlewicz, J. S."},{"last_name":"Handberg","first_name":"R.","full_name":"Handberg, R."},{"last_name":"Tkachenko","first_name":"A.","full_name":"Tkachenko, A."},{"last_name":"Armstrong","full_name":"Armstrong, D. J.","first_name":"D. J."},{"first_name":"M.","full_name":"Hon, M.","last_name":"Hon"},{"last_name":"Kgoadi","full_name":"Kgoadi, R.","first_name":"R."},{"full_name":"Lund, M. N.","first_name":"M. N.","last_name":"Lund"},{"last_name":"Bell","first_name":"K. J.","full_name":"Bell, K. J."},{"orcid":"0000-0003-0142-4000","full_name":"Bugnet, Lisa Annabelle","first_name":"Lisa Annabelle","id":"d9edb345-f866-11ec-9b37-d119b5234501","last_name":"Bugnet"},{"full_name":"Bowman, D. M.","first_name":"D. M.","last_name":"Bowman"},{"last_name":"Johnston","full_name":"Johnston, C.","first_name":"C."},{"last_name":"García","first_name":"R. A.","full_name":"García, R. A."},{"full_name":"Stello, D.","first_name":"D.","last_name":"Stello"},{"last_name":"Molnár","first_name":"L.","full_name":"Molnár, L."},{"last_name":"Plachy","first_name":"E.","full_name":"Plachy, E."},{"first_name":"D.","full_name":"Buzasi, D.","last_name":"Buzasi"},{"last_name":"Aerts","first_name":"C.","full_name":"Aerts, C."}],"_id":"11604","keyword":["Space and Planetary Science","Astronomy and Astrophysics"],"date_published":"2021-10-21T00:00:00Z","day":"21","article_type":"original","year":"2021","publisher":"IOP Publishing","language":[{"iso":"eng"}],"intvolume":"       162","oa_version":"Preprint","issue":"5","title":"TESS Data for Asteroseismology (T’DA) stellar variability classification pipeline: Setup and application to the Kepler Q9 data","scopus_import":"1","quality_controlled":"1","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2107.06301"}]}