Estimating stellar birth radii and the time evolution of the Milky Way's ISM metallicity gradient
I. Minchev, F. Anders, A. Recio-Blanco, C. Chiappini, P. de Laverny,, A. Queiroz, M. Steinmetz, V. Adibekyan, I. Carrillo, G. Cescutti, G., Guiglion, M. Hayden, R. S. de Jong, G. Kordopatis, S. R. Majewski, M. Martig,, B. X. Santiago

TL;DR
This paper introduces a semi-empirical method to estimate the birth radii of Milky Way stars and studies the evolution of the galaxy's metallicity gradient over time, revealing insights into disk formation and chemo-kinematics.
Contribution
The authors develop a model-independent approach to determine stellar birth radii using metallicity and age, constraining the ISM metallicity evolution and chemo-kinematical relations without relying on kinematic data.
Findings
The ISM metallicity gradient flattened from -0.15 to -0.07 dex/kpc over time.
Kinematically hot stars likely formed locally or in the outer disk.
The flat local age-metallicity relation results from superimposed mono-r_birth populations.
Abstract
We present a semi-empirical, largely model-independent approach for estimating Galactic birth radii, r_birth, for Milky Way disk stars. The technique relies on the justifiable assumption that a negative radial metallicity gradient in the interstellar medium (ISM) existed for most of the disk lifetime. Stars are projected back to their birth positions according to the observationally derived age and [Fe/H] with no kinematical information required. Applying our approach to the AMBRE:HARPS and HARPS-GTO local samples, we show that we can constrain the ISM metallicity evolution with Galactic radius and cosmic time, [Fe/H]_ISM(r, t), by requiring a physically meaningful r_birth distribution. We find that the data are consistent with an ISM radial metallicity gradient that flattens with time from ~-0.15 dex/kpc at the beginning of disk formation, to its measured present-day value (-0.07…
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.
