========================================================================== J/A+A/494/739 Automatic classification of OGLE variables (Sarro+, 2009) The following files can be converted to FITS (extension .fit .fgz or .fiZ) list.dat gm/* msbn/* ========================================================================== Query from: http://vizier.u-strasbg.fr/viz-bin/VizieR?-source=J/A+A/494/739 ==========================================================================
drwxr-xr-x 12 cats archive 4096 Jan 29 2012 [Up] drwxr-xr-x 4 cats archive 4096 May 26 08:55 [TAR file] -rw-r--r-- 1 cats archive 479 Apr 11 2009 .message -r--r--r-- 1 cats archive 6737 Apr 11 2009 ReadMe drwxr-xr-x 2 cats archive 4096 Apr 11 2009 gm -r--r--r-- 1 cats archive 1792 Apr 11 2009 list.dat [txt] [txt.gz] [fits] [fits.gz] [html] drwxr-xr-x 2 cats archive 4096 Apr 11 2009 msbn
Beginning of ReadMe : J/A+A/494/739 Automatic classification of OGLE variables (Sarro+, 2009) ================================================================================ Automatic classification of OGLE variables. Sarro L.M., Debosscher J., Lopez M., Aerts C. <Astron. Astrophys. 494, 739 (2009)> =2009A&A...494..739S ================================================================================ ADC_Keywords: Stars, variable ; MK spectral classification Keywords: stars: variables: general - stars: binaries: general - techniques: photometric - methods: data analysis - methods: statistical Abstract: Scientific exploitation of large variability databases can only be fully optimized if these archives contain, besides the actual observations, annotations about the variability class of the objects they contain. Supervised classification of observations produces these tags, and makes it possible to generate refined candidate lists and catalogues suitable for further investigation. 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. Description: Classification probabilities and class assignments are presented for the OGLE Variability database, both on the basis of light curve parameters alone, and in combination with Johnson photometry, for the bulge data and Large and Small Magellanic Clouds.