Automated supervised classification of variable stars I. Methodology

Understand

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.
• 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.