The VVV Templates Project. Towards an Automated Classification of VVV Light-Curves. I. Building a database of stellar variability in the near-infrared

Context. The Vista Variables in the V\'ia L\'actea (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.Comment: 12 pages, 16 figures, 3 tables, accepted for publication in A&A. Most of the data are now accessible through http://www.vvvtemplates.org

The Vista Variables in the Via Lactea (VVV) ESO Public Survey: Current Status and First Results

25 pages, 18 figures. To appear in the Carnegie Observatories Astrophysics Series, Volume 525 pages, 18 figures. To appear in the Carnegie Observatories Astrophysics Series, Volume 5Vista Variables in the Via Lactea (VVV) is an ESO Public Survey that is performing a variability survey of the Galactic bulge and part of the inner disk using ESO's Visible and Infrared Survey Telescope for Astronomy (VISTA). The survey covers 520 deg^2 of sky area in the ZYJHK_S filters, for a total observing time of 1929 hours, including ~ 10^9 point sources and an estimated ~ 10^6 variable stars. Here we describe the current status of the VVV Survey, in addition to a variety of new results based on VVV data, including light curves for variable stars, newly discovered globular clusters, open clusters, and associations. A set of reddening-free indices based on the ZYJHK_S system is also introduced. Finally, we provide an overview of the VVV Templates Project, whose main goal is to derive well-defined light curve templates in the near-IR, for the automated classification of VVV light curves

An improved quasar detection method in EROS-2 and MACHO LMC data sets

International audienceWe present a new classification method for quasar identification in the EROS-2 and MACHO data sets based on a boosted version of a random forest classifier. We use a set of variability features including parameters of a continuous autoregressive model. We prove that continuous autoregressive parameters are very important discriminators in the classification process. We create two training sets (one for EROS-2 and one for MACHO data sets) using known quasars found in the Large Magellanic Cloud (LMC). Our model's accuracy in both EROS-2 and MACHO training sets is about 90 per cent precision and 86 per cent recall, improving the state-of-the-art models, accuracy in quasar detection. We apply the model on the complete, including 28 million objects, EROS-2 and MACHO LMC data sets, finding 1160 and 2551 candidates, respectively. To further validate our list of candidates, we cross-matched our list with 663 previously known strong candidates, getting 74 per cent of matches for MACHO and 40 per cent in EROS. The main difference on matching level is because EROS-2 is a slightly shallower survey which translates to significantly lower signal-to-noise ratio light curves

An improved quasar detection method in EROS-2 and MACHO LMC data sets

We present a new classification method for quasar identification in the EROS-2 and MACHO data sets based on a boosted version of a random forest classifier. We use a set of variability features including parameters of a continuous autoregressive model. We prove that continuous autoregressive parameters are very important discriminators in the classification process. We create two training sets (one for EROS-2 and one for MACHO data sets) using known quasars found in the Large Magellanic Cloud (LMC). Our model's accuracy in both EROS-2 and MACHO training sets is about 90 per cent precision and 86 per cent recall, improving the state-of-the-art models, accuracy in quasar detection. We apply the model on the complete, including 28 million objects, EROS-2 and MACHO LMC data sets, finding 1160 and 2551 candidates, respectively. To further validate our list of candidates, we cross-matched our list with 663 previously known strong candidates, getting 74 per cent of matches for MACHO and 40 per cent in EROS. The main difference on matching level is because EROS-2 is a slightly shallower survey which translates to significantly lower signal-to-noise ratio light curves