4.7 Article

Insights from the APOKASC determination of the evolutionary state of red-giant stars by consolidation of different methods

Journal

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 489, Issue 4, Pages 4641-4657

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/mnras/stz2356

Keywords

asteroseismology; stars: evolution; stars: low-mass; stars: oscillations

Funding

  1. European Research Council (ERC) Consolidator Grant funding scheme (project ASTE-ROCHRONOMETRY) [772293]
  2. Spanish Government [ESP2017-82674-R]
  3. Generalitat de Catalunya [2017-SGR-1131]
  4. Australian Research Council Future Fellowship [FT1400147]
  5. National Aeronautica and Space Administration (NASA) by the Space Telescope Science Institute [51424]
  6. NASA [NAS5-26555]
  7. FCT - Fundao para a Cincia e a Tecnologia
  8. FEDER through COMPETE2020 - Programa Operacional Competitividade e Internacionalizao [PTDC/FIS-AST/30389/2017, POCI-010145-FEDER-030389, UID/FIS/04434/2013, POCI-01-0145FEDER-007672]
  9. European Research Council under the European Community's Seventh Framework Programme (FP7/2007-2013)/ERC [338251]
  10. UK Science and Technology Facilities Council (STFC)
  11. Danish National Research Foundation [DNRF106]
  12. Alfred P. Sloan Foundation
  13. U.S. Department of Energy Office of Science
  14. Brazilian Participation Group
  15. Carnegie Institution for Science, Carnegie Mellon University
  16. Chilean Participation Group
  17. French Participation Group
  18. Harvard-Smithsonian Center for Astrophysics
  19. Johns Hopkins University
  20. Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo
  21. Korean Participation Group
  22. Lawrence Berkeley National Laboratory
  23. Leibniz Institut fur Astrophysik Potsdam (AIP)
  24. MaxPlanck-Institut fur Astronomie (MPIA Heidelberg)
  25. Max-PlanckInstitut fur Astrophysik (MPA Garching)
  26. Max-Planck-Institut fur Extraterrestrische Physik (MPE)
  27. National Astronomical Observatories of China
  28. NewYork University
  29. University of Notre Dame
  30. Observatario Nacional/MCTI
  31. Pennsylvania State University
  32. Shanghai Astronomical Observatory
  33. United Kingdom Participation Group
  34. UniversidadNacionalAutonoma deMexico
  35. University of Arizona
  36. University of Colorado Boulder
  37. University of Oxford
  38. University of Portsmouth
  39. University of Utah
  40. University of Virginia
  41. University of Washington
  42. University of Wisconsin
  43. Vanderbilt University
  44. Yale University
  45. Instituto de Astrof'isica de Canarias
  46. Center for High-Performance Computing at the University of Utah
  47. NewMexico State University
  48. The Ohio State University

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The internal working of low-mass stars is of great significance to both the study of stellar structure and the history of the Milky Way. Asteroseismology has the power to directly sense the internal structure of stars and allows for the determination of the evolutionary state - i.e. has helium burning commenced or is the energy generated only by the fusion in the hydrogen-burning shell? We use observational data from red-giant stars in a combination (known as APOKASC) of asteroseismology (from the Kepler mission) and spectroscopy (from SDSS/APOGEE). The new feature of the analysis is that the APOKASC evolutionary state determination is based on the comparison of diverse approaches to the investigation of the frequency-power spectrum. The high level of agreement between the methods is a strong validation of the approaches. Stars for which there is not a consensus view are readily identified. The comparison also facilitates the identification of unusual stars including those that show evidence for very strong coupling between p and g cavities. The comparison between the classification based on the spectroscopic data and asteroseismic data have led to a new value for the statistical uncertainty in APOGEE temperatures. These consensus evolutionary states will be used as an input for methods that derive masses and ages for these stars based on comparison of observables with stellar evolutionary models ('grid-based modelling') and as a training set for machine-learning and other data-driven methods of evolutionary state determination.

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