Variability classification of CoRoT targets

Although we focus on Giraffe spectra and CoRoT light curves in this work, the methods are much more widely applicable. In order to improve the variability classification obtained from the photometric time series, only rough estimates of the stellar physical parameters (Teff and logg) 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. {gamma} Dor/SPB or {delta} Sct/{beta} 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.