2011A&A...531A.156V

Detection of contaminated light curves and irregular variables has become a challenge when studying variable stars in large photometric surveys such as that produced by the CoRoT mission. Our goal is to characterize and cluster the light curves of the first four runs of CoRoT, in order to find the stars that cannot be classified because of either contamination or exceptional or non-periodic behavior. We study three different approaches to characterize the light curves, namely Fourier parameters, autocorrelation functions (ACF), and hidden Markov models (HMMs). Once the light curves have been transformed into a different input space, they are clustered, using kernel spectral clustering. This is an unsupervised technique based on weighted kernel principal component analysis (PCA) and least squares support vector machine (LS-SVM) formulations. The results are evaluated using the silhouette value. The most accurate characterization of the light curves is obtained by means of HMM. This approach leads to the identification of highly contaminated light curves. After kernel spectral clustering has been implemented onto this new characterization, it is possible to separate the highly contaminated light curves from the rest of the variables. We improve the classification of binary systems and identify some clusters that contain irregular variables. A comparison with supervised classification methods is also presented.