Classification and discovery in large astronomical surveys vol:1082 pages:257-262
AIP CONFERENCE PROCEEDINGS
International Conference on Classification and Discovery in Large Astronomical Surveys Ringberg Castle, GERMANY, OCT 14-17, 2008
The classification of time series from photometric large scale surveys into variability types and the description of their properties is difficult for various reasons including but not limited to the irregular sampling, the usually few available photometric bands, and the diversity of variable objects. Furthermore, it can be seen that diferentphysical processes may sometimes produce similar behavior which may end tip to be represented as same models. In this article we will also be presenting our approach for processing the data resulting from the Gaia space mission. The approach may be classified into following three broader categories: supervised classification, unsupervised classifications, and "so-called" extractor methods i.e. algorithms that are specialized for particular type of sources. The whole process of classification- from classification attribute extraction to actual classification- is done in an automated manner.