Previous studies have found that the elemental abundances of a star correlate directly with its age and metallicity. Using this knowledge, we derive ages for nearly 250,000 stars of the GALAH DR3 sample using only their overall metallicity and chemical abundances. Stellar ages are estimated via the machine learning algorithm XGBoost, using main sequence turnoff stars with precise ages as our input training set. We find that the stellar ages for the bulk of the GALAH DR3 sample are accurate to 1-2 Gyr using this method. With these ages, we replicate many recent results on the age-kinematic trends of the nearby disk, including the age-velocity dispersion relationship of the solar neighborhood and the larger global velocity dispersion relations of the disk found using Gaia and GALAH. The fact that chemical abundances alone can be used to determine a reliable age for a star have profound implications for the future study of the Galaxy as well as upcoming spectroscopic surveys. These results show that the chemical abundance variation at a given birth radius is quite small, and imply that strong chemical tagging of stars directly to birth clusters may prove difficult with our current elemental abundance precision. Our results highlight the need of spectroscopic surveys to deliver precision abundances for as many nucleosynthetic production sites as possible in order to estimate reliable ages for stars directly from their chemical abundances. Applying the methods outlined in this paper opens a new door into studies of the kinematic structure and evolution of the disk, as ages may potentially be estimated for a large fraction of stars in existing spectroscopic surveys. This would yield a sample of millions of stars with reliable age determinations, and allow precise constraints to be put on various kinematic processes in the disk, such as the efficiency and timescales of radial migration.

Using data from the GALAH survey, we explore the dependence of elemental abundances on stellar age and metallicity among Galactic disc stars. We find that the abundance of most elements can be predicted from age and [Fe/H] with an intrinsic scatter of about 0.03 dex. We discuss the possible causes for the existence of the abundance-age-metallicity relations. Using a stochastic chemical enrichment scheme that takes the volume of supernovae remnants into account, we show the intrinsic scatter is expected to be small, about 0.05 dex or even smaller if there is additional mixing in the ISM. Elemental abundances show trends with both age and metallicity and the relationship is well described by a simple model in which the dependence of abundance ([X/Fe]) on age and [Fe/H] are additively separable. Elements can be grouped based on the direction of their abundance gradient in the (age,[Fe/H]) plane and different groups can be roughly associated with three distinct nucleosynthetic production sites, the exploding massive stars, the exploding white dwarfs and the AGB stars. However, the abundances of some elements, like Co, La, and Li, show large scatter for a given age and metallicity, suggesting processes other than simple Galactic chemical evolution are at play. We also compare the abundance trends of main-sequence turn-off stars against that of giants, whose ages were estimated using asteroseismic information from the K2 mission. For most elements, the trends of main-sequence turn-off stars are similar to that of giants. The existence of abundance relations implies that we can estimate the age and birth radius of disc stars, which is important for studying the dynamic and chemical evolution of the Galaxy.

We present the first large-scale study that demonstrates ages are able to be determined for large samples of stars through Galactic chemical evolution. Previous studies found that the elemental abundances of a star correlate directly with its age and metallicity. Using this knowledge, we derive ages for 214,577 stars in GALAH DR3 using only overall metallicities and chemical abundances. Stellar ages are estimated via the machine learning algorithm XGBoost for stars belonging to the Milky Way disk with metallicities between −1 < [Fe/H] < 0.5, using main sequence turnoff stars as our training set. We find that stellar ages for the bulk of GALAH DR3 are precise to 1-2 Gyr using this method. With these ages, we replicate many recent results on the age-kinematic trends of the nearby disk, including the solar neighborhood’s age-velocity dispersion relationship and the larger global velocity dispersion relations of the disk found using Gaia and GALAH. These results show that chemical abundance variations at a given birth radius are small, and imply that strong chemical tagging of stars directly to birth clusters may prove difficult with our current elemental abundance precision. Our results highlight the need to measure abundances for as many nucleosynthetic production sites as possible in order to estimate reliable ages from chemistry. Our methods open a new door into studies of the kinematic structure and evolution of the disk, as ages may potentially be estimated to a precision of 1-2 Gyr for a large fraction of stars in existing spectroscopic surveys.

We present a novel approach to deriving stellar labels for stars observed in MUSE fields making use of data-driven machine learning methods. Taking advantage of the comparable spectral properties (resolution, wavelength coverage) of the LAMOST and MUSE instruments, we adopt the Data-Driven Payne (DD-Payne) model used on LAMOST observations and apply it to stars observed in MUSE fields. Remarkably, in spite of instrumental differences, according to the cross-validation of 27 LAMOST-MUSE common stars, we are able to determine stellar labels with precision better than 75K in Teff, 0.15 dex in log g, and 0.1 dex in abundances of [Fe/H], [Mg/Fe], [Si/Fe], [Ti/Fe], [C/Fe], [Ni/Fe] and [Cr/Fe] for current MUSE observations over a parameter range of 3800 < Teff < 7000 K, −1.5 < [Fe/H] < 0.5 dex. To date, MUSE has been used to target 13,000 fields across the southern sky since it was first commissioned six years ago and it is unique in its ability to study dense star fields such as globular clusters or the Milky Way bulge. Our method will enable the automated determination of stellar parameters for all stars in these fields. Additionally, it opens the door for applications to data collected by other spectrographs having resolution similar to LAMOST. With the upcoming BlueMUSE and MAVIS, we will gain access to a whole new range of chemical abundances with higher precision, especially critical s-process elements such as [Y/Fe] and [Ba/Fe] that provide key age diagnostics for stellar targets.