KIC 12519445 , the SIMBAD biblio

We present the results of an automated variability analysis of the Kepler public data measured in the first quarter (Q1) of the mission. In total, about 150000 light curves have been analysed to detect stellar variability and to identify new members of known variability classes. We also focus on detecting variables present in eclipsing binary systems, given the strong constraints on stellar fundamental parameters they can provide. The methodology we use here is based on the automated variability classification pipeline, which was previously developed for and successfully applied to the CoRoT exofield database and to the limited subset of a few thousand Kepler asteroseismology light curves. We use a Fourier decomposition of the light curves to describe their variability behaviour and use the resulting parameters to perform a supervised classification. Several improvements were made, including a separate extractor method to detect the presence of eclipses when other variability is present in the light curves. We also included two new variability classes compared to previous work: variables showing signs of rotational modulation and of activity. Statistics are given on the number of variables and the number of good candidates per class. A comparison is made with results obtained for the CoRoT exoplanet data. We present some special discoveries, including variable stars in eclipsing binary systems. Many new candidate non-radial pulsators are found, mainly δ Sct and γ Dor stars. We studied those samples in more detail by using 2MASS colours, and the full classification results are made available as an online catalogue.