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Stellar Variability in the VVV Survey: An Update

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 نشر من قبل Marcio Catelan
 تاريخ النشر 2014
  مجال البحث فيزياء
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The Vista Variables in the Via Lactea (VVV) ESO Public Survey consists in a near-infrared time-series survey of the Galactic bulge and inner disk, covering 562 square degrees of the sky, over a total timespan of more than 5 years. In this paper, we provide an updated account of the current status of the survey, especially in the context of stellar variability studies. In this sense, we give a first description of our efforts towards the construction of the VVV Variable Star Catalog (VVV-VSC).



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Context. The Vista Variables in the Via Lactea (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 wi ll 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.
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