Improved variability classification of CoRoT targets with Giraffe spectra^⋆,⋆⋆

¹ Dpt. de Inteligencia Artificial, UNED, Juan del Rosal, 16, 28040 Madrid, Spain
e-mail: lsb@dia.uned.es
² Instituut voor Sterrenkunde, KU Leuven, Celestijnenlaan 200D, 3001 Leuven, Belgium
e-mail: jonas@ster.kuleuven.be
³ LESIA, UMR 8109 du CNRS, Observatoire de Paris, UPMC, Univ. Paris Diderot, 5 place Jules Janssen, 92195 Meudon Cedex, France
⁴ Dept. of Construction and Industrial Manufacturing, University of Oviedo, 33203 Gijón, Spain
⁵ Department of Mechanical Engineering, University of La Rioja, c/ Luis de Ulloa, 20, 26004 Logroño, Spain
⁶ PMQ Research Team; ETSII; Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28016 Madrid, Spain
e-mail: j.ordieres@upm.es
⁷ Department of Astrophysics, IMAPP, Radboud University Nijmegen, PO Box 9010, 6500 GL Nijmegen, The Netherlands

Received: 7 August 2012
Accepted: 16 December 2012

Abstract

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 Giraffe 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 (T_eff 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. T_eff 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.