@article{11604,
  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.},
  author       = {Audenaert, J. and Kuszlewicz, J. S. and Handberg, R. and Tkachenko, A. and Armstrong, D. J. and Hon, M. and Kgoadi, R. and Lund, M. N. and Bell, K. J. and Bugnet, Lisa Annabelle and Bowman, D. M. and Johnston, C. and García, R. A. and Stello, D. and Molnár, L. and Plachy, E. and Buzasi, D. and Aerts, C.},
  issn         = {1538-3881},
  journal      = {The Astronomical Journal},
  keywords     = {Space and Planetary Science, Astronomy and Astrophysics},
  number       = {5},
  publisher    = {IOP Publishing},
  title        = {{TESS Data for Asteroseismology (T’DA) stellar variability classification pipeline: Setup and application to the Kepler Q9 data}},
  doi          = {10.3847/1538-3881/ac166a},
  volume       = {162},
  year         = {2021},
}

@article{13459,
  abstract     = {The B emission-line stars are rapid rotators that were probably spun up by mass and angular momentum accretion through mass transfer in an interacting binary. Mass transfer will strip the donor star of its envelope to create a small and hot subdwarf remnant. Here we report on Hubble Space Telescope/STIS far-ultraviolet spectroscopy of a sample of Be stars that reveals the presence of the hot sdO companion through the calculation of cross-correlation functions of the observed and model spectra. We clearly detect the spectral signature of the sdO star in 10 of the 13 stars in the sample, and the spectral signals indicate that the sdO stars are hot, relatively faint, and slowly rotating as predicted by models. A comparison of their temperatures and radii with evolutionary tracks indicates that the sdO stars occupy the relatively long-lived, He-core burning stage. Only 1 of the 10 detections was a known binary prior to this investigation, which emphasizes the difficulty of finding such Be+sdO binaries through optical spectroscopy. However, these results and others indicate that many Be stars probably host hot subdwarf companions.},
  author       = {Wang, Luqian and Gies, Douglas R. and Peters, Geraldine J. and Götberg, Ylva Louise Linsdotter and Chojnowski, S. Drew and Lester, Kathryn V. and Howell, Steve B.},
  issn         = {1538-3881},
  journal      = {The Astronomical Journal},
  keywords     = {Space and Planetary Science, Astronomy and Astrophysics},
  number       = {5},
  publisher    = {American Astronomical Society},
  title        = {{The detection and characterization of Be+sdO binaries from HST/STIS FUV spectroscopy}},
  doi          = {10.3847/1538-3881/abf144},
  volume       = {161},
  year         = {2021},
}