Automated supervised classification of variable stars II. Application to the OGLE database

Abstract

We aim to extend and test the classifiers presented in a previous work against an independent dataset. We complement the assessment of the validity of the classifiers by applying them to the set of OGLE light curves treated as variable objects of unknown class. The results are compared to published classification results based on the so-called extractor methods.Two complementary analyses are carried out in parallel. In both cases, the original time series of OGLE observations of the Galactic bulge and Magellanic Clouds are processed in order to identify and characterize the frequency components. In the first approach, the classifiers are applied to the data and the results analyzed in terms of systematic errors and differences between the definition samples in the training set and in the extractor rules. In the second approach, the original classifiers are extended with colour information and, again, applied to OGLE light curves. We have constructed a classification system that can process huge amounts of time series in negligible time and provide reliable samples of the main variability classes. We have evaluated its strengths and weaknesses and provide potential users of the classifier with a detailed description of its characteristics to aid in the interpretation of classification results. Finally, we apply the classifiers to obtain object samples of classes not previously studied in the OGLE database and analyse the results. We pay specific attention to the B-stars in the samples, as their pulsations are strongly dependent on metallicity.

42 pages, 39 figures. Accepted for publication in Astronomy and Astrophysics

References in corpus (18)

• Interstellar Dust Grains
• A Quantitative Comparison of SMC, LMC, and Milky Way UV to NIR Extinction Curves
• A new Cepheid distance to the maser-host galaxy NGC 4258 and its implications for the Hubble Constant
• Automated supervised classification of variable stars I. Methodology
• Asteroseismology and Interferometry
• Statistical properties of a sample of periodically variable B-type supergiants - Evidence for opacity-driven gravity-mode oscillations
• Most Detects G- and P-Modes in the B Supergiant HD 163899 (B2Ib/II)
• Extinction Map of the Galactic center: OGLE-II Galactic bulge fields
• Mira Variables in the OGLE Bulge fields
• Revised instability domains of SPB and beta Cephei stars
• Catalog of fundamental mode RR Lyrae stars in the Galactic bulge from the Optical Gravitational Lensing Experiment
• Mira variables in the Galactic bulge with OGLE-II data
• Pulsating B and Be stars in the Small Magellanic Cloud
• Early-type variables in the Magellanic Clouds. I. Beta Cephei stars in the LMC bar
• VSOP: The Variable Star One-shot Project I. Project presentation and first data release
• Automatic classification of eclipsing binaries light curves using neural networks
• Photometric detection of high proper motions in dense stellar fields using Difference Image Analysis
• Eclipsing binaries in the Galactic Bulge: candidates for distance estimates

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