Uniting Gaia and APOGEE to unveil the cosmic chemistry of the Milky Way disc

¹ Max-Planck-Institut für Astronomie, Königstuhl 17, 69117 Heidelberg, Germany
e-mail: cantat@mpia.de
² Leiden Observatory, Leiden University, Niels Bohrweg 2, 2333 CA Leiden, The Netherlands
³ INAF – Osservatorio Astrofisico di Torino, Strada Osservatorio 20, 10025 Pino Torinese, Torino, Italy
⁴ Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
⁵ School of Physics & Astronomy, Monash University, Clayton, 3800 Victoria, Australia
⁶ Centre of Excellence for Astrophysics in Three Dimensions (ASTRO-3D), Melbourne, Victoria, Australia

Received: 18 September 2023
Accepted: 14 December 2023

Abstract

The spatial distribution of Galactic stars with different chemical abundances encodes information on the processes that drove the formation and evolution of the Milky Way. Survey selection functions are indispensable for analysing astronomical catalogues produced by large-scale surveys. The use of these selection functions in data modelling is more complex when data from different surveys are to be modelled simultaneously. We introduce a procedure for constructing the selection function of a sample of red clump stars that have parallaxes and elemental abundances from the Gaia mission. We separately constructed the selection function of the APOGEE DR17 red clump stars, which depends on very different observables and has a very different spatial coverage. We combined the two surveys and accounted for their joint selection function to provide strong constraints on the radial and vertical density distribution of mono-abundance populations, with Gaia offering a dense coverage of the solar neighbourhood, while APOGEE reaches larger distances near the Galactic plane. We confirm that the radial density profile steepens with increasing metallicity. The combined sample also indicates a metallicity-dependent flaring of the α-poor disc. We provide the code for constructing the Gaia selection function we used in this study through the GaiaUnlimited Python package.