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The RAVE Survey: Rich in Very Metal-Poor Stars

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 Added by Jon Fulbright
 Publication date 2010
  fields Physics
and research's language is English




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Very metal-poor stars are of obvious importance for many problems in chemical evolution, star formation, and galaxy evolution. Finding complete samples of such stars which are also bright enough to allow high-precision individual analyses is of considerable interest. We demonstrate here that stars with iron abundances [Fe/H] < -2 dex, and down to below -4 dex, can be efficiently identified within the Radial Velocity Experiment (RAVE) survey of bright stars, without requiring additional confirmatory observations. We determine a calibration of the equivalent width of the Calcium triplet lines measured from the RAVE spectra onto true [Fe/H], using high spectral resolution data for a subset of the stars. These RAVE iron abundances are accurate enough to obviate the need for confirmatory higher-resolution spectroscopy. Our initial study has identified 631 stars with [Fe/H] <= -2, from a RAVE database containing approximately 200,000 stars. This RAVE-based sample is complete for stars with [Fe/H] < -2.5, allowing statistical sample analysis. We identify three stars with [Fe/H] <= -4. Of these, one was already known to be `ultra metal-poor, one is a known carbon-enhanced metal-poor star, but we obtain [Fe/H]= -4.0, rather than the published [Fe/H]=-3.3, and derive [C/Fe] = +0.9, and [N/Fe] = +3.2, and the third is at the limit of our S/N. RAVE observations are on-going and should prove to be a rich source of bright, easily studied, very metal-poor stars.



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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.
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