TOPoS VI. The metal-weak tail of the metallicity distribution functions of the Milky Way and of the Gaia-Sausage-Enceladus structure
P Bonifacio (GEPI), L Monaco (UNAB), S Salvadori, E Caffau (GEPI), M, Spite (GEPI), L Sbordone (ESO), F Spite (GEPI), H.-G Ludwig (LSW), P Di, Matteo (GEPI), M Haywood (GEPI), P Fran\c{c}ois (GEPI), A. J. Koch-Hansen, (ZAH), N Christlieb (LSW), S Zaggia (OAPD)

TL;DR
This study constructs the metallicity distribution function of the Milky Way, especially its metal-weak tail, using a large sample of turn-off stars from SDSS and Gaia data, and analyzes the Gaia-Sausage-Enceladus structure's metallicity properties.
Contribution
It provides a corrected, unbiased MDF of the Milky Way and GSE, combining multiple surveys to characterize the metal-weak tail and compare progenitor galaxy properties.
Findings
The spectroscopic sample is biased towards metal-poor stars.
The MDF of GSE is similar to other surveys, with the progenitor galaxy being metal-poor.
The combined MDFs provide a robust estimate of the metal-weak tail.
Abstract
Context. The TOPoS project has the goal 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 have relied on low-resolution spectra from the Sloan Digital Sky Survey. Aims. In this paper we use the metallicity estimates we have 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 make 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 is used to derive the metallicity bias of the sample for which SDSS spectra are available. Results. We…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
