Variability type classification of multi-epoch surveys

Abstract

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 different physical processes may sometimes produce similar behavior which may end up 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.

6 pages, 2 figures. Version with figures as sent to the Editor/AIP (though not as published). Minor corrections made

Cited by in corpus (3)

• On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data
• Construction of a Calibrated Probabilistic Classification Catalog: Application to 50k Variable Sources in the All-Sky Automated Survey
• Automated Classification of Periodic Variable Stars detected by the Wide-field Infrared Survey Explorer