Examinando por Autor "Pichara, K."

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    Photometric classification of quasars from RCS-2 using Random Forest
    (EDP Sciences, 2015-12) Carrasco, D.; Barrientos L., F.; Pichara, K.; Anguita, T.; Murphy D., N.A.; Gilbank D., G.; Gladders M., D.; Yee H.K., C.; Hsieh B., C.; Lopez, S.
    The classification and identification of quasars is fundamental to many astronomical research areas. Given the large volume of photometric survey data available in the near future, automated methods for doing so are required. In this article, we present a new quasar candidate catalog from the Red-Sequence Cluster Survey 2 (RCS-2), identified solely from photometric information using an automated algorithm suitable for large surveys. The algorithm performance is tested using a well-defined SDSS spectroscopic sample of quasars and stars. The Random Forest algorithm constructs the catalog from RCS-2 point sources using SDSS spectroscopically-confirmed stars and quasars. The algorithm identifies putative quasars from broadband magnitudes (g, r, i, z) and colors. Exploiting NUV GALEX measurements for a subset of the objects, we refine the classifier by adding new information. An additional subset of the data with WISE W1 and W2 bands is also studied. Upon analyzing 542 897 RCS-2 point sources, the algorithm identified 21 501 quasar candidates with a training-set-derived precision (the fraction of true positives within the group assigned quasar status) of 89.5% and recall (the fraction of true positives relative to all sources that actually are quasars) of 88.4%. These performance metrics improve for the GALEX subset: 6529 quasar candidates are identified from 16 898 sources, with a precision and recall of 97.0% and 97.5%, respectively. Algorithm performance is further improved when WISE data are included, with precision and recall increasing to 99.3% and 99.1%, respectively, for 21 834 quasar candidates from 242 902 sources. We compiled our final catalog (38 257) by merging these samples and removing duplicates. An observational follow up of 17 bright (r < 19) candidates with long-slit spectroscopy at DuPont telescope (LCO) yields 14 confirmed quasars. The results signal encouraging progress in the classification of point sources with Random Forest algorithms to search for quasars within current and future large-area photometric surveys. © 2015 ESO.
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    The VVV templates project towards an automated classification of VVV light-curves: I. Building a database of stellar variability in the near-infrared
    (EDP Sciences, 2014-07) Angeloni, R.; Contreras Ramos, R.; Catelan, M.; Dékány, I.; Gran, F.; Alonso-García, J.; Hempel, M.; Navarrete, C.; Andrews, H.; Aparicio, A.; Beamín, J.C.; Berger, C.; Borissova, J.; Contreras Peña, C.; Cunial, A.; De Grijs, R.; Espinoza, N.; Eyheramendy, S.; Eyheramendy, S.; Fiaschi, M.; Hajdu, G.; Han, J.; Hełminiak, K.G.; Hempel, A.; Hidalgo, S.L.; Ita, Y.; Jeon Y., -B; Jordán, A.; Kwon, J.; Lee, J.T.; Martín, E.L.; Masetti, N.; Matsunaga, N.; Milone, A.P.; Minniti, D.; Morelli, L.; Murgas, F.; Nagayama, T.; Navarro, C.; Ochner, P.; Pérez, P.; Pichara, K.; Rojas-Arriagada, A.; Roquette, J.; Saito, R.K.; Siviero, A.; Sohn, J.; Sung, H.-I.; Tamura, M.; Tata, R.; Tomasella, L.; Townsend, B.; Whitelock, P.
    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). The VVV survey will eventually deliver a deep near-IR atlas with photometry and positions in five passbands (ZYJHKS) and a catalogue of 1−10 million variable point sources – mostly unknown – that require classifications. Aims. The main goal of the VVV Templates Project, which 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-IR, 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 auto mated classification will be covered in forthcoming papers of the series.