000001996 001__ 1996
000001996 005__ 20160701171656.0
000001996 0247_ $$2DOI$$a10.1051/0004-6361:20077638
000001996 037__ $$aASTROimport-153
000001996 100__ $$aDebosscher, J.
000001996 245__ $$aAutomated supervised classification of variable stars. I. Methodology
000001996 260__ $$c2007
000001996 520__ $$aContext: 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. The documented classification software codes as well as the light curves and the set of classification parameters for the definition stars, are only available in electronic form at the CDS via anonymous ftp to cdsarc.u-strasbg.fr ( or via http://cdsweb.u-strasbg.fr/cgi-bin/qcat?J/A+A/475/1159 
000001996 700__ $$a Sarro, L. M.
000001996 700__ $$a Aerts, C.
000001996 700__ $$a Cuypers, J.
000001996 700__ $$a Vandenbussche, B.
000001996 700__ $$a Garrido, R.
000001996 700__ $$a Solano, E.
000001996 773__ $$c1159-1183$$i3$$pAstronomy and Astrophysics$$v475$$y2007
000001996 85642 $$ahttp://esoads.eso.org/abs/2007A%26A...475.1159D
000001996 905__ $$apublished in
000001996 980__ $$aREFERD