2014A&A...567A.100A


C.D.S. - SIMBAD4 rel 1.7 - 2020.08.07CEST23:25:52

2014A&A...567A.100A - Astronomy and Astrophysics, volume 567A, 100-100 (2014/7-1)

The VVV Templates Project Towards an automated classification of VVV light-curves. I. Building a database of stellar variability in the near-infrared.

ANGELONI R., CONTRERAS RAMOS R., CATELAN M., DEKANY I., GRAN F., ALONSO-GARCIA J., HEMPEL M., NAVARRETE C., ANDREWS H., APARICIO A., BEAMIN J.C., BERGER C., BORISSOVA J., CONTRERAS PENA C., CUNIAL A., DE GRIJS R., ESPINOZA N., EYHERAMENDY S., FERREIRA LOPES C.E., FIASCHI M., HAJDU G., HAN J., HELMINIAK K.G., HEMPEL A., HIDALGO S.L., ITA Y., JEON Y.-B., JORDAN A., KWON J., LEE J.T., MARTIN E.L., MASETTI N., MATSUNAGA N., MILONE A.P., MINNITI D., MORELLI L., MURGAS F., NAGAYAMA T., NAVARRO C., OCHNER P., PEREZ 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. and WHITELOCK P.

Abstract (from CDS):

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). 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. 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. 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. 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.

Abstract Copyright:

Journal keyword(s): stars: variables: general - surveys - techniques: photometric

CDS comments: fig.14, fig. 16 some objects not identifed.

Simbad objects: 40

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Number of rows : 40

N Identifier Otype ICRS (J2000)
RA
ICRS (J2000)
DEC
Mag U Mag B Mag V Mag R Mag I Sp type #ref
1850 - 2020
#notes
1 NAME SMC G 00 52 38.0 -72 48 01   2.79 2.2     ~ 9478 1
2 NAME Magellanic Clouds GrG 03 00 -71.0           ~ 5698 1
3 GD 428 SX* 03 47 19.8771931141 +63 22 42.147345302   13.10   13.26   ~ 98 0
4 NGC 1487 GiP 03 55 46.5 -42 22 01   12.28 11.68 11.74 11.02 ~ 102 0
5 V* RV Tau Mi* 04 47 06.7238218181 +26 10 45.530144720   11.00 9.80     K0 265 0
6 NAME LMC G 05 23 34.6 -69 45 22     0.4     ~ 14874 1
7 V* BD Pup cC* 07 30 38.7011427096 -24 41 15.085659989           ~ 12 0
8 NGC 2429 GiG 07 43 47.5769881942 +52 21 26.947108924   14.7       ~ 15 0
9 NAME Antennae IG 12 01 53.170 -18 52 37.92           ~ 1558 0
10 [FVM2015] 4 RR* 13 22 22.8730078267 -48 19 04.333590866     13.22     ~ 7 0
11 CRTS J132258.8-464725 RR* 13 22 58.7618615717 -46 47 24.324868325     14.44     ~ 8 0
12 Cl* NGC 5139 SAW V179 EB* 13 23 45.3588343832 -48 17 52.598463855           ~ 6 0
13 2MASS J13245672-4749290 EB* 13 24 56.7474639291 -47 49 29.069511510     18.27   17.07 ~ 2 0
14 Cl* NGC 5139 SAW V284 Pu* 13 24 58.5416911464 -47 36 07.414346836     18.56   17.12 ~ 5 0
15 V* V813 Cen dS* 13 26 01.5455990601 -47 36 36.032311983 15.286 15.231 14.927 14.733 14.477 ~ 17 0
16 V* W Vir WV* 13 26 01.9928122043 -03 22 43.429880244   10.33 9.46 9.29 9.393 F2Ibe 280 0
17 NGC 5139 4001 RR* 13 26 25.4921532245 -47 28 23.823269318 15.101 14.987 14.806 14.337 13.989 ~ 23 0
18 Cl* NGC 5139 BPB 227377 RR* 13 26 34.506 -47 26 57.65 14.699 14.142 13.608 13.422 13.146 ~ 17 0
19 NGC 5139 GlC 13 26 47.28 -47 28 46.1   6.12 5.33     ~ 3010 0
20 Cl* NGC 5139 BPB 238719 RR* 13 26 49.653 -47 26 23.84 14.998 14.940 14.705 14.224 14.024 ~ 21 0
21 V* V814 Cen EB* 13 27 44.0322297567 -47 26 09.712309669 14.505 14.271 14.295 13.991 13.838 K2.5 32 0
22 2MASS J13283288-4726242 EB* 13 28 32.88 -47 26 24.2     17.71   16.76 ~ 5 0
23 Cl* NGC 5139 SAW V178 * 13 31 50.1714103830 -47 18 21.827237220           ~ 6 1
24 Cl* NGC 5139 SAW V182 RR* 13 32 13.3076417195 -47 06 18.137969589           ~ 7 0
25 NGC 6134 OpC 16 27 46 -49 09.1   7.89 7.2     ~ 130 0
26 V* WY Sco Ce* 16 33 20.4090209759 -26 11 14.915016729     12.07     ~ 13 0
27 M 62 GlC 17 01 12.60 -30 06 44.5   8.55 7.39     ~ 513 0
28 NAME Gal Center reg 17 45 40.04 -29 00 28.1           ~ 11613 0
29 NGC 6441 GlC 17 50 13.06 -37 03 05.2   9.26 8.00     ~ 770 0
30 V* BL Her WV* 18 01 09.2241552984 +19 14 56.695123489   10.54 9.70     F0 155 0
31 ESO 521-16 GlC 18 02 57.40 -26 04 00.0           ~ 73 0
32 NAME 2MASS-GC02 GlC 18 09 36.5 -20 46 44           ~ 37 0
33 NGC 6638 GlC 18 30 56.25 -25 29 47.1   10.81 9.68     ~ 193 0
34 OGLE BLG566.10 66564 RR* 18 36 19.56 -23 54 32.9 15.15 15.00 14.27   13.26 ~ 7 0
35 M 22 GlC 18 36 23.94 -23 54 17.1   7.16 6.17     ~ 1218 0
36 V* V1355 Aql bL* 19 33 52.2842395055 +15 56 27.423080037   12.65 11.77     ~ 15 0
37 V* IP Peg DN* 23 23 08.5362716048 +18 24 59.206210572           M2 360 0
38 V* SX Phe SX* 23 46 32.8929064612 -41 34 54.770795675   7.40 7.12     A3V 313 0
39 NAME Local Group GrG ~ ~           ~ 7000 0
40 NAME Galactic Bulge reg ~ ~           ~ 3412 0

    Equat.    Gal    SGal    Ecl

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2020.08.07-23:25:52

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