A two-stage settling of the Milky Way disk revealed by precise ChronoGal ages
astro-ph.GA
Submitted: 2026-10-02
Updated: 2026-10-02
Comments: 17 pages, 10 figures, accepted for publication in ApJL
License: http://creativecommons.org/licenses/by/4.0/
The gist: The spatial distribution of Galactic disk stars of different ages and metallicities encodes its dynamical evolution history and formation pathways.
Abstract
The spatial distribution of Galactic disk stars of different ages and metallicities encodes its dynamical evolution history and formation pathways. The lack of precise ages for large, representative samples of stars has traditionally been a drawback for securely differentiating among formation scenarios proposed to explain the vertical properties of the Milky Way disk (e.g. upside-down formation versus secular dynamical heating). In this Letter, we use CMD-fitting of Gaia data to derive very precise age-metallicity distributions for complete stellar samples in the solar neighborhood. We identify quasi-mono-age-metallicity stellar populations with an age resolution better than 5 % (about 0.5 Gyr at old age), and study their absolute vertical density profile. We observe a clear correlation between scale height and stellar age, with younger populations consistently more confined to the plane. We find evidence that the scale height decrease rate changed drastically 10.85+0.13-0.12 Gyr ago: at older ages, the evolution is very steep, with h Z dropping from over 1.26 kpc to about 0.5 kpc in merely 2 Gyr; subsequently, the thinning is very gradual, with h Z about 0.1 kpc for the youngest populations. The extremely rapid early vertical settling of the disk points to a fundamentally upside-down formation process at early times, when the thick disk component is basically formed. We thus strongly support an evolutionary scenario defined by two distinct temporal phases: an initial, highly turbulent epoch that gave birth to the thick disk, and a subsequent more quiescent period of secular evolution and gradual gas settling which formed the thin disk.
Sources
- Scikit-learn: Machine Learning in Python
- The Age-Thickness Relation as a Tracer of the Merger History of Disk Galaxies
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