The VVV Templates Project. Towards an Automated Classification of VVV Light-Curves. I. Building a database of stellar variability in the near-infrared
arXiv:1405.4517 · doi:10.1051/0004-6361/201423904
Abstract
Context. The Vista Variables in the Vía Láctea (VVV) ESO Public Survey is a variability survey of the Milky Way bulge and an adjacent section of the disk carried out from 2010 on ESO Visible and Infrared Survey Telescope for Astronomy (VISTA). VVV will eventually deliver a deep near-IR atlas with photometry and positions in five passbands (ZYJHK_S) and a catalogue of 1-10 million variable point sources - mostly unknown - which require classifications. Aims. The main goal of the VVV Templates Project, that we introduce in this work, is to develop and test the machine-learning algorithms for the automated classification of the VVV light-curves. As VVV is the first massive, multi-epoch survey of stellar variability in the near-infrared, the template light-curves that are required for training the classification algorithms are not available. In the first paper of the series we describe the construction of this comprehensive database of infrared stellar variability. Methods. First we performed a systematic search in the literature and public data archives, second, we coordinated a worldwide observational campaign, and third we exploited the VVV variability database itself on (optically) well-known stars to gather high-quality infrared light-curves of several hundreds of variable stars. Results. We have now collected a significant (and still increasing) number of infrared template light-curves. This database will be used as a training-set for the machine-learning algorithms that will automatically classify the light-curves produced by VVV. The results of such an automated classification will be covered in forthcoming papers of the series.
12 pages, 16 figures, 3 tables, accepted for publication in A&A. Most of the data are now accessible through http://www.vvvtemplates.org/
References in corpus (8)
- The WFCAM Science Archive
- Automated supervised classification of variable stars I. Methodology
- Near-Infrared Photometry of Carbon Stars
- The period-luminosity relation for type II Cepheids in globular clusters
- A Deep Wide-Field Variable Star Catalog of Omega Centauri
- An improved quasar detection method in EROS-2 and MACHO LMC datasets
- A multicolor near-infrared study of the dwarf nova IP Peg
- Near-infrared photometry of Galactic planetary nebulae with the VVV Survey
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