Abstract

Context. The goal of the Turn-Off Primordial Stars survey (TOPoS) project is to find and analyse turn-off (TO) stars of extremely low metallicity. To select the targets for spectroscopic follow-up at high spectral resolution, we relied on low-resolution spectra from the Sloan Digital Sky Survey (SDSS). Aims. In this paper, we use the metallicity estimates we obtained from our analysis of the SDSS spectra to construct the metallicity distribution function (MDF) of the Milky Way, with special emphasis on its metal-weak tail. The goal is to provide the underlying distribution out of which the TOPoS sample was extracted. Methods. We made use of SDSS photometry, Gaia photometry, and distance estimates derived from the Gaia parallaxes to derive a metallicity estimate for a large sample of over 24 million TO stars. This sample was used to derive the metallicity bias of the sample for which SDSS spectra are available. Results. We determined that the spectroscopic sample is strongly biased in favour of metal-poor stars, as intended. A comparison with the unbiased photometric sample allows us to correct for the selection bias. We selected a sub-sample of stars with reliable parallaxes for which we combined the SDSS radial velocities with Gaia proper motions and parallaxes to compute actions and orbital parameters in the Galactic potential. This allowed us to characterise the stars dynamically, and in particular to select a sub-sample that belongs to the Gaia-Sausage-Enceladus (GSE) accretion event. We are thus also able to provide the MDF of GSE. Conclusions. The metal-weak tail derived in our study is very similar to that derived in the H3 survey and in the Hamburg/ESO Survey. This allows us to average the three MDFs and provide an error bar for each metallicity bin. Inasmuch as the GSE structure is representative of the progenitor galaxy that collided with the Milky Way, that galaxy appears to be strongly deficient in metal-poor stars compared to the Milky Way, suggesting that the metal-weak tail of the latter has been largely formed by accretion of low-mass galaxies rather than massive galaxies, such as the GSE progenitor.

Highlights

  • Considerable information on a galaxy’s formation and evolution is imprinted in its metallicity distribution function (MDF)

  • Inasmuch as the GSE structure is representative of the progenitor galaxy that collided with the Milky Way, that galaxy appears to be strongly deficient in metal-poor stars compared to the Milky Way, suggesting that the metal-weak tail of the latter has been largely formed by accretion of low-mass galaxies rather than massive galaxies, such as the GSE progenitor

  • We used our own analysis of Sloan Digital Sky Survey (SDSS) spectra in order to determine the MDF of the metal-poor component of the Milky Way

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Summary

Introduction

Considerable information on a galaxy’s formation and evolution is imprinted in its metallicity distribution function (MDF). From the low-resolution spectra, they were able to extract metallicities using a spectral index centred on the Ca ii H&K lines, which resulted in a catalogue of 134 metal-poor stars. This sample extended down to metallicity −4.0 (star CS 22876−32). Carney et al (1987) demonstrated how metallicities could be reliably derived from high-resolution (R ∼ 30 000), low signalto-noise spectra, resulting in a catalogue of 818 stars that provided an MDF extending to −3.0 (Laird et al 1988). Distances based on Gaia parallaxes allowed us to derive a photometric metallicity estimate from SDSS photometry This was used to quantify the metallicity bias present in our SDSS spectroscopic sample.

Selection and analysis of SDSS spectra
Precision
Accuracy
Cross-match with Gaia and down selection
SDSS DR12 selected
Bias in the observed spectroscopic metallicity distribution function
Photometric metallicities
Comparison with a larger photometric sample
The MDF
Comparison with H3
The metal-weak tail of the MDF
Dynamical properties of the SDSS spectroscopic sample
The Gaia-Sausage-Enceladus structure
50 Good parallax
Errors on dynamical quantities and consequences on selections
Findings
Discussion
10. Conclusions
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