Very metal-poor stars observed by the RAVE survey

Abstract

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 $([Fe/H]<-2\mathrm{dex})$, out of which 43 are likely extremely metal-poor $([Fe/H]<-3\mathrm{dex})$. 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 ${\sigma }_{[Fe/H]}\approx 0.2\mathrm{dex}$. 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.

Accepted by A&A

References in corpus (23)

• The Radial Velocity Experiment (RAVE): first data release
• Nucleosynthetic signatures of the first stars
• The Radial Velocity Experiment (RAVE): Fifth Data Release
• A Search for Stars of Very Low Metal Abundance. VI. Detailed Abundances of 313 Metal-Poor Stars
• The Radial Velocity Experiment (RAVE): second data release
• The NIR Ca II triplet at low metallicity - Searching for extremely low-metallicity stars in classical dwarf galaxies
• ARES v2 - new features and improved performance
• Analysis and calibration of CaII triplet spectroscopy of Red Giant Branch stars from VLT/FLAMES observations
• The stellar content of the Hamburg/ESO survey. IV. Selection of candidate metal-poor stars
• Bright Metal-Poor Stars from the Hamburg/ESO Survey. I. Selection and Follow-up Observations from 329 Fields
• Automated derivation of stellar atmospheric parameters and chemical abundances: the MATISSE algorithm
• Calcium II Triplet Spectroscopy of LMC Red Giants. I. Abundances and Velocities for a Sample of Populous Clusters
• Stellar Archaeology -- Exploring the Universe with Metal-Poor Stars
• APASS Landolt-Sloan BVgri photometry of RAVE stars. I. Data, effective temperatures and reddenings
• Precise time-series photometry for the Kepler-2.0 mission
• Large eccentricity, low mutual inclination: the three-dimensional architecture of a hierarchical system of giant planets
• The Best and Brightest Metal-Poor Stars
• Radial Velocity Observations and Light Curve Noise Modeling Confirm That Kepler-91b is a Giant Planet Orbiting a Giant Star
• The Infrared Ca II triplet as metallicity indicator
• The Gaia-ESO Survey: the most metal-poor stars in the Galactic bulge
• Double-lined Spectroscopic Binary Stars in the Radial Velocity Experiment Survey
• New Approaches To Photometric Redshift Prediction Via Gaussian Process Regression In The Sloan Digital Sky Survey
• An equatorial ultra iron-poor star identified in BOSS

Cited by in corpus (25)

• The Sixth Data Release of the Radial Velocity Experiment (RAVE) -- I: Survey Description, Spectra and Radial Velocities
• The R-Process Alliance: First Release from the Northern Search for r-Process Enhanced Metal-Poor Stars in the Galactic Halo
• The $R$-Process Alliance: Fourth Data Release from the Search for $r$-Process-Enhanced Stars in the Galactic Halo
• StarHorse results for spectroscopic surveys + Gaia DR3: Chrono-chemical populations in the solar vicinity, the genuine thick disk, and young-alpha rich stars
• Dissecting stellar chemical abundance space with t-SNE
• The age of the Milky Way inner stellar spheroid from RR Lyrae population synthesis
• The GALAH survey: Chemical Tagging of Star Clusters and New Members in the Pleiades
• The r-Process Pattern of a Bright, Highly r-Process-Enhanced, Metal-Poor Halo Star at [Fe/H] ~ -2
• The RAdial Velocity Experiment (RAVE): Parameterisation of RAVE spectra based on convolutional neural networks
• The R-Process Alliance: Discovery of a low-alpha, r-Process-Enhanced Metal-Poor Star in the Galactic Halo
• Machine learning in APOGEE: Identification of stellar populations through chemical abundances
• The Gaia-ESO Survey: Preparing the ground for 4MOST & WEAVE galactic surveys. Chemical evolution of lithium with machine learning
• The Gaia-ESO Survey: Chemical evolution of Mg and Al in the Milky Way with Machine-Learning
• The Stars of the HETDEX Survey. I. Radial Velocities and Metal-Poor Stars from Low-Resolution Stellar Spectra
• Classification of High-resolution Solar Hα Spectra using t-distributed Stochastic Neighbor Embedding
• Exploring the chemodynamics of metal-poor stellar populations
• Climbing the cosmic ladder with stellar twins in RAVE with Gaia
• The Pristine survey -- XX: GTC follow-up observations of extremely metal-poor stars identified from Pristine and LAMOST
• Masses and ages for metal-poor stars: a pilot program combining asteroseismology and high-resolution spectroscopic follow-up of RAVE halo stars
• Spectroscopic follow-up of statistically selected extremely metal-poor star candidates from GALAH DR3
• High-resolution observations of two pores with the integral field unit (IFU) of the GREGOR Infrared Spectrograph (GRIS)
• RAVE-Gaia and the impact on Galactic archeology
• The GALAH Survey: A New Sample of Extremely Metal-Poor Stars Using A Machine Learning Classification Algorithm
• Galactic Archeology with RAVE and TGAS
• Playing CHESS with stars. I. Search for similar stars in large spectroscopic data sets