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

R. Angeloni, R. Contreras Ramos, M. Catelan, I. Dékány, F. Gran, J. Alonso-García, M. Hempel, C. Navarrete, H. Andrews, A. Aparicio, J. C. Beamín, C. Berger, J. Borissova, C. Contreras Peña, A. Cunial, R. De Grijs, N. Espinoza, S. Eyheramendy, C. E. Ferreira Lopes, M. FiaschiG. Hajdu, J. Han, K. G. Hełminiak, A. Hempel, S. L. Hidalgo, Y. Ita, Y. B. Jeon, A. Jordán, J. Kwon, J. T. Lee, E. L. Martín, N. Masetti, N. Matsunaga, A. P. Milone, D. Minniti, L. Morelli, F. Murgas, T. Nagayama, C. Navarro, P. Ochner, P. Pérez, K. Pichara, A. Rojas-Arriagada, J. Roquette, R. K. Saito, A. Siviero, J. Sohn, H. I. Sung, M. Tamura, R. Tata, L. Tomasella, B. Townsend, P. Whitelock

Resultado de la investigación: Article

22 Citas (Scopus)

Resumen

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 (ZYJHK S) 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 automated classification will be covered in forthcoming papers of the series.

Idioma originalEnglish
Número de artículoA100
PublicaciónAstronomy and Astrophysics
Volumen567
DOI
EstadoPublished - 1 ene 2014

Huella dactilar

light curve
near infrared
templates
machine learning
European Southern Observatory
astronomy
education
atlas
point source
project
variable stars
point sources
catalogs
photometry
time measurement
telescopes
stars
public

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

Citar esto

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. ; Ferreira Lopes, C. E. ; 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. / The VVV Templates Project Towards an automated classification of VVV light-curves : I. Building a database of stellar variability in the near-infrared. En: Astronomy and Astrophysics. 2014 ; Vol. 567.
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title = "The VVV Templates Project Towards an automated classification of VVV light-curves: I. Building a database of stellar variability in the near-infrared",
abstract = "Context. The Vista Variables in the V{\'i}a L{\'a}ctea (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 (ZYJHK S) 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 automated classification will be covered in forthcoming papers of the series.",
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author = "R. Angeloni and {Contreras Ramos}, R. and M. Catelan and I. D{\'e}k{\'a}ny and F. Gran and J. Alonso-Garc{\'i}a and M. Hempel and C. Navarrete and H. Andrews and A. Aparicio and Beam{\'i}n, {J. C.} and C. Berger and J. Borissova and {Contreras Pe{\~n}a}, C. and A. Cunial and {De Grijs}, R. and N. Espinoza and S. Eyheramendy and {Ferreira Lopes}, {C. E.} and M. Fiaschi and G. Hajdu and J. Han and Hełminiak, {K. G.} and A. Hempel and Hidalgo, {S. L.} and Y. Ita and Jeon, {Y. B.} and A. Jord{\'a}n and J. Kwon and Lee, {J. T.} and Mart{\'i}n, {E. L.} and N. Masetti and N. Matsunaga and Milone, {A. P.} and D. Minniti and L. Morelli and F. Murgas and T. Nagayama and C. Navarro and P. Ochner and P. P{\'e}rez and K. Pichara and A. Rojas-Arriagada and J. Roquette and Saito, {R. K.} and A. Siviero and J. Sohn and Sung, {H. I.} and M. Tamura and R. Tata and L. Tomasella and B. Townsend and P. Whitelock",
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doi = "10.1051/0004-6361/201423904",
language = "English",
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journal = "Astronomy and Astrophysics",
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Angeloni, R, Contreras Ramos, R, Catelan, M, Dékány, I, Gran, F, Alonso-García, J, Hempel, M, Navarrete, C, Andrews, H, Aparicio, A, Beamín, JC, Berger, C, Borissova, J, Contreras Peña, C, Cunial, A, De Grijs, R, Espinoza, N, Eyheramendy, S, Ferreira Lopes, CE, Fiaschi, M, Hajdu, G, Han, J, Hełminiak, KG, Hempel, A, Hidalgo, SL, Ita, Y, Jeon, YB, Jordán, A, Kwon, J, Lee, JT, Martín, EL, Masetti, N, Matsunaga, N, Milone, AP, Minniti, D, Morelli, L, Murgas, F, Nagayama, T, Navarro, C, Ochner, P, Pérez, P, Pichara, K, Rojas-Arriagada, A, Roquette, J, Saito, RK, Siviero, A, Sohn, J, Sung, HI, Tamura, M, Tata, R, Tomasella, L, Townsend, B & Whitelock, P 2014, 'The VVV Templates Project Towards an automated classification of VVV light-curves: I. Building a database of stellar variability in the near-infrared', Astronomy and Astrophysics, vol. 567, A100. https://doi.org/10.1051/0004-6361/201423904

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.; 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.; Ferreira Lopes, C. E.; 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.

En: Astronomy and Astrophysics, Vol. 567, A100, 01.01.2014.

Resultado de la investigación: Article

TY - JOUR

T1 - The VVV Templates Project Towards an automated classification of VVV light-curves

T2 - I. Building a database of stellar variability in the near-infrared

AU - Angeloni, R.

AU - Contreras Ramos, R.

AU - Catelan, M.

AU - Dékány, I.

AU - Gran, F.

AU - Alonso-García, J.

AU - Hempel, M.

AU - Navarrete, C.

AU - Andrews, H.

AU - Aparicio, A.

AU - Beamín, J. C.

AU - Berger, C.

AU - Borissova, J.

AU - Contreras Peña, C.

AU - Cunial, A.

AU - De Grijs, R.

AU - Espinoza, N.

AU - Eyheramendy, S.

AU - Ferreira Lopes, C. E.

AU - Fiaschi, M.

AU - Hajdu, G.

AU - Han, J.

AU - Hełminiak, K. G.

AU - Hempel, A.

AU - Hidalgo, S. L.

AU - Ita, Y.

AU - Jeon, Y. B.

AU - Jordán, A.

AU - Kwon, J.

AU - Lee, J. T.

AU - Martín, E. L.

AU - Masetti, N.

AU - Matsunaga, N.

AU - Milone, A. P.

AU - Minniti, D.

AU - Morelli, L.

AU - Murgas, F.

AU - Nagayama, T.

AU - Navarro, C.

AU - Ochner, P.

AU - Pérez, P.

AU - Pichara, K.

AU - Rojas-Arriagada, A.

AU - Roquette, J.

AU - Saito, R. K.

AU - Siviero, A.

AU - Sohn, J.

AU - Sung, H. I.

AU - Tamura, M.

AU - Tata, R.

AU - Tomasella, L.

AU - Townsend, B.

AU - Whitelock, P.

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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 (ZYJHK S) 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 automated classification will be covered in forthcoming papers of the series.

AB - 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 (ZYJHK S) 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 automated classification will be covered in forthcoming papers of the series.

KW - general

KW - photometric

KW - Stars

KW - Surveys

KW - Techniques

KW - variables

UR - http://www.scopus.com/inward/record.url?scp=84904620989&partnerID=8YFLogxK

U2 - 10.1051/0004-6361/201423904

DO - 10.1051/0004-6361/201423904

M3 - Article

AN - SCOPUS:84904620989

VL - 567

JO - Astronomy and Astrophysics

JF - Astronomy and Astrophysics

SN - 0004-6361

M1 - A100

ER -