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Very Metal-poor Stars Observed by the RAVE Survey

Published online by Cambridge University Press:  09 May 2016

Gal Matijevič
Affiliation:
Leibniz-Institut für Astrophysik Potsdam (AIP), An der Sternwarte 16, 14482 Potsdam, Germany email: [email protected]
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Abstract

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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.

Type
Contributed Papers
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