Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-29T10:24:19.606Z Has data issue: false hasContentIssue false
  • English
  • Français

Metallicity distribution functions using Gaia-DR1 data

Published online by Cambridge University Press:  07 March 2018

Georges Kordopatis Open the ORCID record for Georges Kordopatis [Opens in a new window]  and
RAVE collaboration
Georges Kordopatis
Affiliation:
Université Côte d'Azur, Observatoire de la Côte d'Azur, CNRS, Laboratoire Lagrange, France email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The metallicity distribution function (MDF) of the stellar components of the Milky Way hold valuable information regarding the processes that have taken place in the evolution of our Galaxy. In this proceeding, we investigate updates concerning the MDF now that the Tycho-Gaia Astrometric Solution (TGAS) catalogue has been released and that trigonometric distances are available. In particular, vertical changes and skewness of the MDF are investigated, together with the properties of the metal-rich stars in the sample, at different positions in the Galaxy.

Keywords

Milky Waystellar contentevolutionmetallicity
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S330: Astrometry and Astrophysics in the Gaia sky , April 2017 , pp. 172 - 175
Copyright
Copyright © International Astronomical Union 2018 

References

Bensby, T., et al., 2011, ApJ, 735, 46 Google Scholar
Bland-Hawthorn, J. & Gerhard, O., 2016, ARA&A, 54, 561 Google Scholar
Bovy, J., et al., 2016, ApJ, 823, 30 CrossRefGoogle Scholar
Bovy, J., 2017, MNRAS, submitted, arXiv:1704.05063Google Scholar
Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2016, A&A, 595, A2 Google Scholar
Gaia Collaboration, Prusti, T., de Bruijne, J. H. J., et al. 2016, A&A, 595, A1 Google Scholar
Hayden, M., et al., 2015, ApJ, 808, 132 CrossRefGoogle Scholar
Kordopatis, G., et al., 2011, A&A, 535, A107 Google Scholar
Kordopatis, G., et al., 2013, AJ, 146, 134 Google Scholar
Kordopatis, G., et al., 2015, MNRAS, 447, 3526 CrossRefGoogle Scholar
Kordopatis, G., et al. 2017, MNRAS, 467, 469 Google Scholar
Kunder, A., Kordopatis, G., Steinmetz, M., et al. 2017, AJ, 153, 75 Google Scholar
Lindegren, L., et al., 2016, A&A, 595, A4 Google Scholar
Minchev, I., Chiappini, C., & Martig, M., 2014, A&A, 572, 92 Google Scholar
Pont, F., Eyer, L., 2004, MNRAS, 351, 487 CrossRefGoogle Scholar
Sellwood, J. A. & Binney, J. J., 2002, MNRAS, 336, 785 Google Scholar
Schlesinger, K., et al., 2014, ApJ, 791, 112 Google Scholar
Wojno, J., Kordopatis, G., Piffl, T., et al. 2017, MNRAS, 468, 3368 CrossRefGoogle Scholar
Zacharias, N., Finch, C., Frouard, J., 2017, AJ, 153, 166 Google Scholar