Stellar Age Compression Reshapes Interpretations of the Milky Way Thick-Disk Formation History

Fuente: arXiv
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Main Author: Zhang, Zhipeng
Format: Preprint
Published: 2026
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author Zhang, Zhipeng
author_facet Zhang, Zhipeng
contents The formation timescale of the Milky Way thick disk is one of the central debates in Galactic archaeology. The age-metallicity relation (AMR), formation timescale, and chemical evolution gradients are frequently used to infer a rapid assembly, short-timescale enrichment, and bursty formation history of the thick disk. However, stellar ages are not directly observable, introducing the potential risk that inferred ages may harbor a systematic compression tied to observational quality. In this paper, we use the same stellar sample and identical physical covariate matching conditions, but two independent age scales--spectroscopic inferred ages (astroNN) and asteroseismic ages (APOKASC-3)--to compare the observable signatures of the thick-disk formation history. We find that several key observables previously supporting a rapid thick-disk formation are systematically weakened under seismic anchoring: the AMR slope flattens from -3.29 to -1.86 Gyr dex-1 (Delta a = +1.43), the formation timescale widens from 3.04 to 3.55 Gyr, and the peak formation age shifts from 9.1 to 6.0 Gyr. Through transport inversion experiments, we further show that additive noise can only broaden the age distribution and cannot reproduce the above pattern, whereas a compressive transport map (lambda < 1) simultaneously reproduces a narrower age distribution, a steeper AMR, and rapid-formation-like observables. This result indicates that the compression transformation itself is sufficient to generate rapid-formation-friendly observables without requiring an intrinsically bursty formation history. Our findings reveal that statistical interpretations of the Milky Way formation history may depend sensitively on the stellar age definition itself.
format Preprint
id arxiv_https___arxiv_org_abs_2605_10220
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Stellar Age Compression Reshapes Interpretations of the Milky Way Thick-Disk Formation History
Zhang, Zhipeng
Astrophysics of Galaxies
Machine Learning
The formation timescale of the Milky Way thick disk is one of the central debates in Galactic archaeology. The age-metallicity relation (AMR), formation timescale, and chemical evolution gradients are frequently used to infer a rapid assembly, short-timescale enrichment, and bursty formation history of the thick disk. However, stellar ages are not directly observable, introducing the potential risk that inferred ages may harbor a systematic compression tied to observational quality. In this paper, we use the same stellar sample and identical physical covariate matching conditions, but two independent age scales--spectroscopic inferred ages (astroNN) and asteroseismic ages (APOKASC-3)--to compare the observable signatures of the thick-disk formation history. We find that several key observables previously supporting a rapid thick-disk formation are systematically weakened under seismic anchoring: the AMR slope flattens from -3.29 to -1.86 Gyr dex-1 (Delta a = +1.43), the formation timescale widens from 3.04 to 3.55 Gyr, and the peak formation age shifts from 9.1 to 6.0 Gyr. Through transport inversion experiments, we further show that additive noise can only broaden the age distribution and cannot reproduce the above pattern, whereas a compressive transport map (lambda < 1) simultaneously reproduces a narrower age distribution, a steeper AMR, and rapid-formation-like observables. This result indicates that the compression transformation itself is sufficient to generate rapid-formation-friendly observables without requiring an intrinsically bursty formation history. Our findings reveal that statistical interpretations of the Milky Way formation history may depend sensitively on the stellar age definition itself.
title Stellar Age Compression Reshapes Interpretations of the Milky Way Thick-Disk Formation History
topic Astrophysics of Galaxies
Machine Learning
url https://arxiv.org/abs/2605.10220