Comparative clustering analysis of variable stars in the Hipparcos, OGLE Large Magellanic Cloud, and CoRoT exoplanet databases

Abstract

Context. Discovery of new variability classes in large surveys using multivariate statistics techniques such as clustering, relies heavily on the correct understanding of the distribution of known classes as point processes in parameter space. Aims. Our objective is to analyze the correspondence between the classical stellar variability types and the clusters found in the distribution of light curve parameters and colour indices of stars in the CoRoT exoplanet sample. The final aim is to help in the identification on new types of variability by first identifying the well known variables in the CoRoT sample. Methods. We apply unsupervised classification algorithms to identify clusters of variable stars from modes of the probability density distribution. We use reference variability databases (Hipparcos and OGLE) as a framework to calibrate the clustering methodology. Furthermore, we use the results from supervised classification methods to interpret the resulting clusters. Results. We interpret the clusters in the Hipparcos and OGLE LMC databases in terms of large-amplitude radial pulsators in the classical instability strip and of various types of eclipsing binaries. The Hipparcos data also provide clear distributions for low-amplitude nonradial pulsators. We show that the preselection of targets for the CoRoT exoplanet programme results in a completely different probability density landscape than the OGLE data, the interpretation of which involves mainly classes of low-amplitude variability in main-sequence stars. Our findings will be incorporated to improve the supervised classification used in the CoRoT catalogue production, once the existence of new classes or subtypes will be confirmed from complementary spectroscopic observations.

Classification of variable stars in the WFCAM Transit Survey

“…What we can observe is that whereas for low frequencies it is not trivial for any clear relation to be detected, the high-frequency domain is dominated by eclipsing binaries of all three classes with 21 ≈ 0. This is in accordance with the results from the analysis of the Hipparcos archive presented in [10].…”

“…Hot Planets and Cool Stars Previous studies [9,10] have demonstrated that various parameters resulting from the analysis of the light curves can be used to successfully classify the objects in one class or another. For example, optical data from Hipparcos, OGLE and CoRoT have been used for the determination of robust sets of parameters derived from the light curves of known member stars of various variability classes.…”

Comparative Analysis of a Transition Region Bright Point with a Blinker and Coronal Bright Point Using Multiple EIS Emission Lines

“…Recent development of modern machine learning techniques and high performance computing architectures have made possible the efficient execution of automated probabilistic multi-class classification of very large datasets in reasonable time frames (Debosscher et al, 2007;Sarro et al, 2009aSarro et al, , 2009bDebosscher et al, 2009;Richards et al, 2011Richards et al, , 2012Blomme et al, 2011;Matijevič et al, 2012;Morgan et al, 2012;Long et al, 2012). An essential step in the development of such a framework is processing a large set of events of known class to train the classifier.…”

Global stellar variability study in the field-of-view of theKeplersatellite

“…We keep improving the capabilities of the classifiers, and have now extended our training set to be able to recognize light curves showing the signs of rotational modulation and activity. We used the clustering results obtained from the CoRoT data, as presented in Sarro et al (2009), to define these two new classes. Their template data consist of CoRoT exoplanet field light curves for now, but they will be extended in the future, since Kepler will provide many new examples.…”