Automated supervised classification of variable stars

Abstract

Context. The fast classification of new variable stars is an important step in making them available for further research. Selection of science targets from large databases is much more efficient if they have been classified first. Defining the classes in terms of physical parameters is also important to get an unbiased statistical view on the variability mechanisms and the borders of instability strips. Aims. Our goal is twofold: provide an overview of the stellar variability classes that are presently known, in terms of some relevant stellar parameters; use the class descriptions obtained as the basis for an automated "supervised classification" of large databases. Such automated classification will compare and assign new objects to a set of pre-defined variability training classes. Methods. For every variability class, a literature search was performed to find as many well-known member stars as possible, or a considerable subset if too many were present. Next, we searched on-line and private databases for their light curves in the visible band and performed period analysis and harmonic fitting. The derived light curve parameters are used to describe the classes and define the training classifiers. Results. We compared the performance of different classifiers in terms of percentage of correct identification, of confusion among classes and of computation time. We describe how well the classes can be separated using the proposed set of parameters and how future improvements can be made, based on new large databases such as the light curves to be assembled by the CoRoT and Kepler space missions. Conclusions. The derived classifiers' performances are so good in terms of success rate and computational speed that we will evaluate them in practice from the application of our methodology to a large subset of variable stars in the OGLE database and from comparison of the results with published OGLE variable star classifications based on human intervention. These results will be published in a subsequent paper.

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

“…The entire Hipparcos variability catalogue was processed as described in Debosscher et al (2007), and light curves with p-values, as defined in Debosscher et al (2009), below 10 7 were retained for clustering analysis. Furthermore, we made use of class labels in order to interpret the resulting clusters.…”

New insights into time series analysis

“…Automatic classifiers based on machine learning have been applied to several large time-series data sets (e.g. Woźniak et al 2004;Debosscher et al 2007;Sarro et al 2009;Blomme et al 2010;Richards et al 2011;Dubath et al 2011). The inclusion of periodic and non-periodic features, statistics, and more sophisticated model parameters have improved automatic classifiers (e.g.…”

Radial velocity confirmation of Kepler-91 b

“…T max = t max − t min = 62 days, where t max and t min are the earliest and latest Julian dates in our observations). Since the observations are unevenly separated, the shortest period searched was set to the median of the inverse time interval between data points, as was proposed by Debosscher et al (2007) and Ivezić et al (2013), T min = Δt = 2.8 days 1 . The significant peak in the power spectrum (with a false-alarm probability 2 of FAP = 0.09%, over the 0.1% level) coincides with the expected period of the planet.…”