Very metal-poor stars observed by the RAVE survey ^⋆

¹ Leibniz-Institut für Astrophysik Potsdam (AIP), An der Sternwarte 16, 14482 Potsdam, Germany
e-mail: gmatijevic@aip.de
² Astronomisches Rechen-Institut, Zentrum für Astronomie der Universität Heidelberg, Mönchhofstr. 12–14, 69120 Heidelberg, Germany
³ Department of Physics and Astronomy, Johns Hopkins University, 3400 N. Charles St, Baltimore, MD 21218, USA
⁴ Faculty of Mathematics and Physics, University of Ljubljana, 1000 Ljubljana, Slovenia
⁵ Observatoire astronomique de Strasbourg, Université de Strasbourg, CNRS, UMR 7550, 11 rue de l’Université, 67000 Strasbourg, France
⁶ Sydney Institute for Astronomy, School of Physics A28, University of Sydney, NSW 2006, Australia
⁷ Research School of Astronomy and Astrophysics, Australian National University, Cotter Rd, Weston, ACT 2611, Australia
⁸ E. A. Milne Centre for Astrophysics, University of Hull, Hull, HU6 7RX, UK
⁹ Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
¹⁰ Kapteyn Astronomical Institute, University of Groningen, PO Box 800, 9700 AV Groningen, The Netherlands
¹¹ Saint Martin’s University, Old Main, 5000 Abbey Way SE, Lacey, WA 98503, USA
¹² INAF Astronomical Observatory of Padova, 36012 Asiago (VI), Italy
¹³ Department of Physics and Astronomy, University of Victoria, Victoria, BC, Canada V8P 5C2, Canada
¹⁴ Department of Physics, Chong Yuet Ming Physics Building, The University of Hong Kong, Hong Kong
¹⁵ Department of Physics and Astronomy, Macquarie University, Sydney, NSW 2109, Australia
¹⁶ Western Sydney University, Locked Bag 1797, Penrith South DC, NSW 1797, Australia
¹⁷ Mullard Space Science Laboratory, University College London, Holmbury St. Mary, Dorking, RH5 6NT, UK
¹⁸ Dipartimento di Fisica e Astronomia Galileo Galilei, Universita’ di Padova, Vicolo dell’Osservatorio 3, 35122 Padova, Italy
¹⁹ Australian Astronomical Observatory, North Ryde, NSW 2113, Australia

Received: 10 January 2017
Accepted: 30 March 2017

Abstract

Metal-poor stars trace the earliest phases in the chemical enrichment of the Universe. They give clues about the early assembly of the Galaxy as well as on the nature of the first stellar generations. Multi-object spectroscopic surveys play a key role in finding these fossil records in large volumes. Here we present a novel analysis of the metal-poor star sample in the complete Radial Velocity Experiment (RAVE) Data Release 5 catalog with the goal of identifying and characterizing all very metal-poor stars observed by the survey. Using a three-stage method, we first identified the candidate stars using only their spectra as input information. We employed an algorithm called t-SNE to construct a low-dimensional projection of the spectrum space and isolate the region containing metal-poor stars. Following this step, we measured the equivalent widths of the near-infrared Ca ii triplet lines with a method based on flexible Gaussian processes to model the correlated noise present in the spectra. In the last step, we constructed a calibration relation that converts the measured equivalent widths and the color information coming from the 2MASS and WISE surveys into metallicity and temperature estimates. We identified 877 stars with at least a 50% probability of being very metal-poor ([Fe/H] < −2 dex), out of which 43 are likely extremely metal-poor ([Fe/H] < −3 dex). The comparison of the derived values to a small subsample of stars with literature metallicity values shows that our method works reliably and correctly estimates the uncertainties, which typically have valuesσ_[Fe/H] ≈ 0.2 dex. In addition, when compared to the metallicity results derived using the RAVE DR5 pipeline, it is evident that we achieve better accuracy than the pipeline and therefore more reliably evaluate the very metal-poor subsample. Based on the repeated observations of the same stars, our method gives very consistent results. We intend to study the identified sample further by acquiring high-resolution spectroscopic follow-up observations. The method used in this work can also easily be extended to other large-scale data sets, including to the data from the Gaia mission and the upcoming 4MOST survey.