Stellar Age Compression Reshapes Interpretations of the Milky Way Thick-Disk Formation History
Authors: Zhipeng Zhang
Organizations: China Mobile Research Institute, Beijing 100053, China · China Mobile GBA (Greater Bay Area) Innovation Institute, Guangzhou 510656, China
Abstract
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.
Statistical inference in observational science typically relies on a fundamental assumption: as sample size increases and uncertainties decrease, the inferred results should converge to the true physical quantities. This assumption underpins the notion that big data lead to more reliable conclusions. In Galactic archaeology, stellar ages inferred from spectroscopic surveys are widely used to reconstruct the formation history of the Milky Way disk. The age metallicity relation (AMR) and its derived formation timescale are often regarded as key physical diagnostics of early disk evolution. This interpretation carries an implicit premise: that observational quality does not introduce systematic bias into age inference. Here we show that this premise may fail. Using a large sample of subgiant stars, we identify a region in the observational quality parameter space (signal-to-noise ratio and parallax precision) where the inferred formation timescale exhibits a systematic offset of 0.5-1 Gyr relative to an independent asteroseismic reference, while the statistical uncertainties remain small, thus producing a stable-but-wrong inference state.
Close-in exoplanets exhibit a wide range of orbital architectures and physical properties shaped by both formation conditions and migration processes. Although population-synthesis models predict distinct planetary populations, establishing a quantitative connection between observed exoplanets and synthetic populations remains challenging. We investigate the intrinsic organisation of close-in exoplanets using physically motivated dynamical parameters and connect the resulting populations to pebble-accretion formation pathways. A two-stage Gaussian mixture model (GMM) is applied to an observed sample of close-in exoplanets, performing unsupervised probabilistic clustering in a feature space dominated by dynamical descriptors of planet-star interactions. The resulting clusters are mapped onto a pebble-accretion synthetic population within a statistically motivated three-dimensional parameter space. Formation-related quantities, including gas availability, gas fraction, and ice-rock mass ratio, are then used to interpret the mapped populations. We identify statistically supported sub-populations without imposing predefined classification boundaries, including very-massive gas giants, hot giants, warm-Jupiter-dominated systems, and lower-mass giants. The mapped synthetic populations reveal systematic differences in formation timing, gas accretion, and solid growth histories. In particular, very-massive gas giants are preferentially associated with earlier formation epochs than hot-giant and warm-Jupiter-dominated populations. These results demonstrate that physically motivated machine-learning approaches can provide a statistically robust framework for linking observed exoplanet populations to theoretical planet formation pathways.
The local dark matter density determines the strength of the signal expected in direct-detection experiments, yet published estimates from stellar motions disagree by more than their errors, and the most recent machine-learning analysis of Gaia data finds a local density consistent with zero. According to Jeans' theorem, a distribution function built from integrals of motion satisfies the collisionless Boltzmann equation (CBE) trivially for any choice of potential, so a search that simultaneously fits the distribution function and the potential to the CBE identifies neither. Our pipeline instead estimates the distribution function in isolation, linearizing the equation in terms of accelerations and allowing for direct measurement of the local force field, and then fits closed forms to that field via symbolic regression. Throughout, we find that the usable information lies not in the CBE residual but in the stellar number counts, the observable most distorted by survey selection. Along the vertical profile, our recovered potential agrees with the classical self-gravitating isothermal disc.