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


Query : 2014A&A...567A.100A

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 - 2024
#notes
1 NAME SMC G 00 52 38.0 -72 48 01   2.79 2.2     ~ 11146 1
2 NAME Magellanic Clouds GrG 03 00 -71.0           ~ 7065 0
3 GD 428 SX* 03 47 19.8773016336 +63 22 42.150015336   13.10   13.26   ~ 100 0
4 NGC 1487 GiP 03 55 46.5 -42 22 01   12.28 11.68 11.74 11.02 ~ 111 0
5 V* RV Tau RV* 04 47 06.7239850584 +26 10 45.532913268   11.00 9.80     K3Ibe 279 1
6 NAME LMC G 05 23 34.6 -69 45 22     0.4     ~ 17432 0
7 V* BD Pup cC* 07 30 38.7012376896 -24 41 15.086861160     13.614   12.127 ~ 21 0
8 NGC 2429 GiG 07 43 47.5775925120 +52 21 26.944226208   14.7       ~ 17 0
9 NAME Antennae IG 12 01 53.170 -18 52 37.92           ~ 1691 0
10 Cl* NGC 5139 SAW V175 RR* 13 22 22.8730398888 -48 19 04.334562228     13.22     ~ 8 0
11 Cl* NGC 5139 SAW V171 RR* 13 22 58.7619874944 -46 47 24.324762948     14.44     ~ 10 0
12 Cl* NGC 5139 SAW V179 EB* 13 23 45.3589905192 -48 17 52.599079896           ~ 6 0
13 Cl* NGC 5139 WSB V97 EB* 13 24 56.7462799608 -47 49 29.067398292     18.27   17.07 ~ 2 0
14 Cl* NGC 5139 SAW V284 Pu* 13 24 58.5426312120 -47 36 07.406937396     18.56   17.12 ~ 5 0
15 V* V813 Cen dS* 13 26 01.5456979560 -47 36 36.031730832 15.286 15.231 14.927 14.733 14.477 ~ 17 0
16 V* W Vir WV* 13 26 01.9928807928 -03 22 43.431062880   10.33 9.46 9.29 9.393 F2Ibe 300 0
17 NGC 5139 4001 RR* 13 26 25.4972004624 -47 28 23.723871780 15.101 14.987 14.806 14.337 13.989 ~ 23 0
18 Cl* NGC 5139 BPB 227377 RR* 13 26 34.5030420072 -47 26 57.765571368 14.699 14.142 13.608 13.422 13.146 ~ 20 0
19 NGC 5139 GlC 13 26 47.28 -47 28 46.1           ~ 3426 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 ~ 22 0
21 V* V814 Cen EB* 13 27 44.0322800448 -47 26 09.713693796 14.505 14.271 14.295 13.991 13.838 K2.5 32 0
22 Cl* NGC 5139 SAW V245 EB* 13 28 32.88 -47 26 24.2     17.71   16.76 ~ 5 0
23 Cl* NGC 5139 SAW V178 * 13 31 50.1713254968 -47 18 21.825907200           ~ 6 1
24 Cl* NGC 5139 SAW V182 RR* 13 32 13.3186414008 -47 06 18.059247324           ~ 8 0
25 NGC 6134 OpC 16 27 48.7 -49 09 40   7.89 7.2     ~ 152 0
26 V* WY Sco WV* 16 33 20.4089925984 -26 11 14.914705812     12.07     ~ 13 0
27 M 62 GlC 17 01 12.60 -30 06 44.5           ~ 619 0
28 NAME Galactic Center reg 17 45 39.60213 -29 00 22.0000           ~ 14414 0
29 NGC 6441 GlC 17 50 13.06 -37 03 05.2           ~ 884 0
30 V* BL Her WV* 18 01 09.2240599584 +19 14 56.696475012   10.54 9.70     F0 172 0
31 ESO 521-16 GlC 18 02 57.40 -26 04 00.0           ~ 99 0
32 NAME 2MASS-GC02 GlC 18 09 36.5 -20 46 44           ~ 53 0
33 NGC 6638 GlC 18 30 56.25 -25 29 47.1   10.81 9.68     ~ 234 0
34 OGLE BLG566.10 66564 RR* 18 36 19.5575569584 -23 54 32.900552532 15.15 15.00 14.27   13.26 ~ 11 0
35 M 22 GlC 18 36 23.94 -23 54 17.1           ~ 1378 0
36 V* V1355 Aql EB* 19 33 52.2832468632 +15 56 27.430287612   12.65 11.77     ~ 16 0
37 V* IP Peg CV* 23 23 08.5362249504 +18 24 59.206884012           M2 381 0
38 V* SX Phe SX* 23 46 32.8928138474 -41 34 54.769886474   7.54 7.31     A3V 341 0
39 NAME Local Group GrG ~ ~           ~ 8389 0
40 NAME Galactic Bulge reg ~ ~           ~ 4299 0

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