Very metal-poor stars observed by the RAVE survey
  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 CaII 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
$(\rm [Fe/H] < -2\,\rm dex)$, out of which 43 are likely extremely metal-poor
$(\rm [Fe/H] < -3\,\rm dex )$. 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
values $\sigma_{\rm [Fe/H]} \approx 0.2\,\mathrm{dex}$. 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. 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.
