The Gaia-ESO Survey: the chemical structure of the Galactic discs from the first internal data release
\v{S}. Mikolaitis, V. Hill, A. Recio-Blanco, P. de Laverny, C. Allende, Prieto, G. Kordopatis, G. Tautvai\v{s}iene, D. Romano, G. Gilmore, S., Randich, S. Feltzing, G. Micela, A. Vallenari, E. J. Alfaro, T. Bensby, A., Bragaglia, E. Flaccomio, A. C. Lanzafame, E. Pancino

TL;DR
This study uses Gaia-ESO survey data to analyze the chemical composition and structure of the Milky Way's discs, revealing differences in elemental gradients and spatial distributions between thin and thick discs.
Contribution
It provides the first extensive chemical and spatial analysis of Galactic discs beyond the solar neighborhood using Gaia-ESO data, highlighting differences in gradients and distributions.
Findings
Identification of bimodal [Mg/M] distributions indicating thin and thick disc populations.
Detection of a radial metallicity gradient in the thin disc.
Observation of distinct vertical metallicity and alpha-element gradients between discs.
Abstract
Most high-resolution spectroscopic studies of the Galactic discs were mostly confined to objects in the solar vicinity. Here we aim at enlarging the volume in which individual chemical abundances are used to characterise both discs, using the first internal data release of the Gaia-ESO survey. We derive and discuss the abundances of eight elements (Mg, Al, Si, Ca, Ti, Fe, Cr, Ni, and Y). The trends of these elemental abundances with iron are very similar to those in the solar neighbourhood. We find a natural division between alpha-rich and alpha-poor stars, best seen in the bimodality of the [Mg/M] distributions in bins of metallicity, which we attribute to thick- and thin-disc sequences, respectively. With the possible exception of Al, the observed dispersion around the trends is well described by the expected errors, leaving little room for astrophysical dispersion. Using previously…
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.
