Hostname: page-component-76fb5796d-r6qrq Total loading time: 0 Render date: 2024-04-25T11:10:16.588Z Has data issue: false hasContentIssue false
  • English
  • Français

Very Metal-poor Stars Observed by the RAVE Survey

Published online by Cambridge University Press:  09 May 2016

Gal Matijevič  and
the RAVE Collaboration
Gal Matijevič
Affiliation:
Leibniz-Institut für Astrophysik Potsdam (AIP), An der Sternwarte 16, 14482 Potsdam, Germany email: gmatijevic@aip.de
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.

Radial Velocity Experiment (RAVE) observed ~500,000 southern sky stars between 2003 and 2013 in the infra-red calcium triplet (CaII) spectral region. In this study we extended the analysis of RAVE very metal-poor stars ([Fe/H] < −2) presented by Fulbright et al. (2010). We employed a novel method for identifying the metal-poor stars and developed a tool for modeling CaII lines where we also modeled the background noise to avoid systematical biases in the equivalent width (EW) measurements. Final metallicity values were derived with a flexible calibration approach using only 2MASS photometric data and EW measurements obtained from the RAVE spectra.

Keywords

Metal-poor starsRAVE surveyGaussian processes
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 11 , Issue S317: The General Assembly of Galaxy Halos: Structure, Origin and Evolution , August 2015 , pp. 336 - 337
Copyright
Copyright © International Astronomical Union 2016 

References

Beers, T. C. & Christlieb, N. 2005, ARA&A, 43, 531Google Scholar
Carrera, R., Pancino, E., Gallart, C., et al. 2013, MNRAS, 434, 1681CrossRefGoogle Scholar
Fulbright, J. P., Wyse, R. F. G., & Ruchti, G. R., et al. 2010, ApJ, 724, L104Google Scholar
Kordopatis, G., Gilmore, G., Steinmetz, M., et al. 2013, AJ, 146, 134Google Scholar
Matijevič, G., Zwitter, T., Bienaym, O., et al. 2012, ApJS, 200, 14Google Scholar
Ness, M., Hogg, D. W., & Rix, H.-W., et al. 2015, ApJ, 808, 16Google Scholar
Rasmussen, C. E. & Williams, C. 2006, MIT PressGoogle Scholar
Ruchti, G. R., Bergemann, M., Serenelli, A., et al. 2013, MNRAS, 429, 126Google Scholar
van der Maaten, L. J. P. 2014, Journal of Machine Learning Research 15 3221?Google Scholar