Improved variability classification of CoRoT targets with Giraffe spectra

  1. Sarro, L.M. 4
  2. Debosscher, J. 5
  3. Neiner, C. 6
  4. Bello-García, A. 3
  5. González-Marcos, A. 2
  6. Prendes-Gero, B. 3
  7. Ordieres, J. 7
  8. León, Gonzalo. 4
  9. Aerts, C. 15
  10. De Batz, B. 6
  1. 1 Radboud University Nijmegen

    Radboud University Nijmegen

    Nimega, Holanda


  2. 2 Universidad de La Rioja

    Universidad de La Rioja

    Logroño, España


  3. 3 Universidad de Oviedo

    Universidad de Oviedo

    Oviedo, España


  4. 4 Universidad Nacional de Educación a Distancia

    Universidad Nacional de Educación a Distancia

    Madrid, España


  5. 5 Instituut voor Sterrenkunde, KU Leuven, Celestijnenlaan 200D, 3001 Leuven, Belgium
  6. 6 Laboratory of Space Studies and Instrumentation in Astrophysics

    Laboratory of Space Studies and Instrumentation in Astrophysics

    Meudon, Francia


  7. 7 Universidad Politécnica de Madrid

    Universidad Politécnica de Madrid

    Madrid, España


Astronomy and astrophysics

ISSN: 0004-6361

Year of publication: 2013

Volume: 550

Type: Article

DOI: 10.1051/0004-6361/201220184 SCOPUS: 2-s2.0-84879661040 WoS: WOS:000314879700120 GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Astronomy and astrophysics

Institutional repository: lock_openOpen access editor


Cited by

  • Scopus Cited by: 19 (09-03-2023)
  • Web of Science Cited by: 16 (09-03-2023)

JCR (Journal Impact Factor)

  • Year 2013
  • Journal Impact Factor: 4.479
  • Journal Impact Factor without self cites: 3.067
  • Article influence score: 1.722
  • Best Quartile: Q1
  • Area: ASTRONOMY & ASTROPHYSICS Quartile: Q1 Rank in area: 13/59 (Ranking edition: SCIE)

SCImago Journal Rank

  • Year 2013
  • SJR Journal Impact: 2.747
  • Best Quartile: Q1
  • Area: Space and Planetary Science Quartile: Q1 Rank in area: 14/110
  • Area: Astronomy and Astrophysics Quartile: Q1 Rank in area: 12/108

Scopus CiteScore

  • Year 2013
  • CiteScore of the Journal : 8.5
  • Area: Space and Planetary Science Percentile: 87
  • Area: Astronomy and Astrophysics Percentile: 83


Aims.We present an improved method for automated stellar variability classification, using fundamental parameters derived from high resolution spectra, with the goal to improve the variability classification obtained using information derived from CoRoT light curves only. Although we focus on Girae spectra and CoRoT light curves in this work, the methods are much more widely applicable. Methods. In order to improve the variability classification obtained from the photometric time series, only rough estimates of the stellar physical parameters (Teff and log (g)) are needed because most variability types that overlap in the space of time series parameters, are well separated in the space of physical parameters (e.g. γ Dor/SPB or δ Sct/β Cep). In this work, several state-of-the-art machine learning techniques are combined to estimate these fundamental parameters from high resolution Giraffe spectra. Next, these parameters are used in a multi-stage Gaussian-Mixture classifier to perform an improved supervised variability classification of CoRoT light curves. The variability classifier can be used independently of the regression module that estimates the physical parameters, so that non-spectroscopic estimates derived e.g. from photometric colour indices can be used instead. Results. Teff and log (g) are derived from Giraffe spectra, for 6832 CoRoT targets. The use of those parameters in addition to information extracted from the CoRoT light curves, significantly improves the results of our previous automated stellar variability classification. Several new pulsating stars are identified with high confidence levels, including hot pulsators such as SPB and β Cep, and several γ Dor-δ Sct hybrids. From our samples of new γ Dor and δ Sct stars, we find strong indications that the instability domains for both types of pulsators are larger than previously thought. © 2013 ESO.