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The variable Universe Through the Eyes of Gaia

Published online by Cambridge University Press:  15 February 2011

L. Eyer
Affiliation:
Observatoire de Genève, Université de Genève, 1290 Sauverny, Switzerland
M. Suveges
Affiliation:
ISDC, Observatoire de Genève, Université de Genève, 1290 Sauverny, Switzerland
P. Dubath
Affiliation:
ISDC, Observatoire de Genève, Université de Genève, 1290 Sauverny, Switzerland
N. Mowlavi
Affiliation:
ISDC, Observatoire de Genève, Université de Genève, 1290 Sauverny, Switzerland
C. Greco
Affiliation:
Observatoire de Genève, Université de Genève, 1290 Sauverny, Switzerland
M. Varadi
Affiliation:
Observatoire de Genève, Université de Genève, 1290 Sauverny, Switzerland
D. W. Evans
Affiliation:
Institute of Astronomy, University of Cambridge, Cambridge CB3 0HA, UK
P. Bartholdi
Affiliation:
Observatoire de Genève, Université de Genève, 1290 Sauverny, Switzerland
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Abstract

The ESA Gaia mission will provide a multi-epoch database for a billion of objects, including variable objects that comprise stars, active galactic nuclei and asteroids. We highlight a few of Gaia’s properties that will benefit the study of variable objects, and illustrate with two examples the work being done in the preparation of the data processing and object characterization. The first example relates to the analysis of the nearly simultaneous multi-band data of Gaia with the Principal Component Analysis techniques, and the second example concerns the classification of Gaia time series into variability types. The results of the ground-based processing of Gaia’s variable objects data will be made available to the scientific community through the intermediate and final ESA releases throughout the mission.

Type
Research Article
Copyright
© EAS, EDP Sciences 2011

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References

Bartholdi, P., 2005, Gaia document: VSWG-PB-001
Breiman, L., 2001, Machine Learning, 45, 5 CrossRef
Eyer, L., & Mignard, F., 2005, MNRAS, 361, 1136 CrossRef
Eyer, L., & Mowlavi, N., 2008, J. Phys. Conf. Ser., 118, 2010 CrossRef
Hastie, T., Tibshirani, R., & Friedman, J., 2009, “The Elements of Statistical Learning”, Springer, ISBN 978-0-387-84857-0
Mignard, F., 2010, June 23–25 Science Alert Workshop presentation (Cambridge, UK)
Sesar, B., et al., 2007, AJ, 134, 2236 CrossRef
Spano, M., Mowlavi, N., Eyer, L., & Burki, G., 2009, AIP Conf. Proc., 1170, 324 CrossRef
Svetnik V., et al., 2004, ed. Roli F., Kittler J., & Windeatt T., Lecture notes in computer science (Springer), 3077, 334

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