Explore the vast range of titles available.

The empirical relations between rotation period, chromospheric activity, and age can be used to estimate stellar age. To calibrate these relations, we present a catalog, including the masses and ages of 52,321 FGK dwarfs, 47,489 chromospheric activity index logRHK+ , 6077 rotation periods P rot, and variability amplitudes S ph, based on data from LAMOST DR7, Kepler, and Gaia Data Release 3. We find a pronounced correlation among P rot, age, and [Fe/H] throughout the main-sequence phase for F dwarfs. However, the decrease of logRHK+ over time is not significant except for those with [Fe/H] < −0.1. For G dwarfs, both P rot and logRHK+ are reliable age probes in the ranges ∼2–11 Gyr and ∼2–13 Gyr, respectively. K dwarfs exhibit a prominent decrease in logRHK+ within the age range of ∼3–13 Gyr when the relation of P rot–τ is invalid. These relations are very important for promptly estimating the age of a vast number of stars, thus serving as a powerful tool in advancing the fields of exoplanet properties, stellar evolution, and Galactic archeology.

Gyrochronology, the field of age dating stars using mainly their rotation periods and masses, is ideal for inferring the ages of individual main-sequence stars. However, due to the lack of physical understanding of the complex magnetic fields in stars, gyrochronology relies heavily on empirical calibrations that require consistent and reliable stellar age measurements across a wide range of periods and masses. In this paper, we obtain a sample of consistent ages using the gyro-kinematic age-dating method, a technique to calculate the kinematics ages of stars. Using a Gaussian process model conditioned on ages from this sample (∼1–14 Gyr) and known clusters (0.67–3.8 Gyr), we calibrate the first empirical gyrochronology relation that is capable of inferring ages for single, main-sequence stars between 0.67 and 14 Gyr. Cross-validating and testing results suggest our model can infer cluster and asteroseismic ages with an average uncertainty of just over 1 Gyr, and the inferred ages for wide binaries agree within 0.83 Gyr. With this model, we obtain gyrochronology ages for ∼100,000 stars within 1.5 kpc of the Sun with period measurements from Kepler and Zwicky Transient Facility and 384 unique planet host stars. A simple code is provided to infer gyrochronology ages of stars with temperature and period measurements.

We present stellar age determinations for 4661 red giant branch stars in the APO-K2 catalog, derived using mass estimates from K2 asteroseismology from the K2 Galactic Archaeology Program and elemental abundances from the Apache Point Galactic Evolution Experiment survey. Our sample includes 17 of the 19 fields observed by K2, making it one of the most comprehensive catalogs of accurate stellar ages across the Galaxy in terms of the wide range of populations spanned by its stars, enabling rigorous tests of Galactic chemical evolution models. Taking into account the selection functions of the K2 sample, the data appear to support the age−chemistry morphology of stellar populations predicted by both inside-out and late-burst scenarios. We also investigate trends in age versus stellar chemistry and Galactic position, which are consistent with previous findings. Comparisons against APOKASC-3 asteroseismic ages show agreement to within ∼3%. We also discuss offsets between our ages and spectroscopic ages. Finally, we note that ignoring the effects of α-enhancement on stellar opacity (either directly or with the Salaris metallicity correction) results in an ∼10% offset in age estimates for the most α-enhanced stars, which is an important consideration for continued tests of Galactic models with this and other asteroseismic age samples.

We present new maps of the Milky Way disk showing the distribution of metallicity ([Fe/H]), α-element abundances ([Mg/Fe]), and stellar age, using a sample of 66,496 red giant stars from the final data release (DR17) of the Apache Point Observatory Galactic Evolution Experiment survey. We measure radial and vertical gradients, quantify the distribution functions for age and metallicity, and explore chemical clock relations across the Milky Way for the low-α disk, high-α disk, and total population independently. The low-α disk exhibits a negative radial metallicity gradient of −0.06 ± 0.001 dex kpc−1, which flattens with distance from the midplane. The high-α disk shows a flat radial gradient in metallicity and age across nearly all locations of the disk. The age and metallicity distribution functions shift from negatively skewed in the inner Galaxy to positively skewed at large radius. Significant bimodality in the [Mg/Fe]–[Fe/H] plane and in the [Mg/Fe]–age relation persist across the entire disk. The age estimates have typical uncertainties of ∼0.15 in log(age) and may be subject to additional systematic errors, which impose limitations on conclusions drawn from this sample. Nevertheless, these results act as critical constraints on galactic evolution models, constraining which physical processes played a dominant role in the formation of the Milky Way disk. We discuss how radial migration predicts many of the observed trends near the solar neighborhood and in the outer disk, but an additional more dramatic evolution history, such as the multi-infall model or a merger event, is needed to explain the chemical and age bimodality elsewhere in the Galaxy.

We present 3D full star simulations, reaching up to 90% of the total stellar radius, for three 7 M ⊙ stars of different ages: zero-age main sequence (ZAMS), mid–main sequence (midMS), and terminal-age main sequence (TAMS). A comparison with several theoretical prescriptions shows that the generation spectra for all three ages are dominated by convective plumes. Two distinct overshooting layers are observed, with most plumes stopped within the layer situated directly above the convective boundary; overshooting to the second, deeper layer becomes progressively more infrequent with increasing stellar age. Internal gravity wave (IGW) propagation is significantly impacted in the midMS and TAMS models as a result of some IGWs getting trapped within their Brunt–Väisälä frequency spikes. A fundamental change in the wave structure across radius is also observed, driven by the effect of density stratification on IGW propagation causing waves to become evanescent within the radiative zone, with older stars being affected more strongly. We find that the steepness of the frequency spectrum at the surface increases from ZAMS to the older models, with older stars also showing more modes in their spectra.

The radius valley, a dip in the radius distribution of exoplanets at ∼1.9 R ⊕, separates compact rocky super-Earths and sub-Neptunes with lower density. Various hypotheses have been put forward to explain the radius valley. Characterizing the radius valley morphology and its correlation to stellar properties will provide crucial observation constraints on its origin mechanism and deepen the understanding of planet formation and evolution. In this paper, the third part of the Planets Across Space and Time series, using the LAMOST-Gaia-Kepler catalog, we perform a systematical investigation into how the radius valley morphology varies in the Galactic context, i.e., thin/thick galactic disks, stellar age, and metallicity abundance ([Fe/H] and [α/Fe]). We find the following: (1) The valley becomes more prominent with the increase of both age and [Fe/H]. (2) The number ratio of super-Earths to sub-Neptunes monotonically increases with age but decreases with [Fe/H] and [α/Fe]. (3) The average radius of planets above the valley (2.1–6 R ⊕) decreases with age but increases with [Fe/H]. (4) In contrast, the average radius of planets below the valley (R < 1.7 R ⊕) is broadly independent of age and metallicity. Our results demonstrate that the valley morphology, as well as the whole planetary radius distribution, evolves on a long timescale of gigayears, and metallicities (not only Fe but also other metal elements, e.g., Mg, Si, Ca, Ti) play important roles in planet formation and in the long-term planetary evolution.

Understanding the ages of stars is crucial for unraveling the formation history and evolution of our Galaxy. Traditional methods for estimating stellar ages from spectroscopic data often struggle with providing appropriate uncertainty estimations and are severely constrained by the parameter space. In this work, we introduce a new approach using normalizing flows—a type of deep generative model—to estimate stellar ages for evolved stars with improved accuracy and robust uncertainty characterization. The model is trained on stellar masses for evolved stars derived from asteroseismology and predicts the relationship between the carbon and nitrogen abundances of a given star and its age. Unlike standard neural network techniques, normalizing flows enable the recovery of full likelihood distributions for individual stellar ages, offering a richer and more informative perspective on uncertainties. Our method yields age estimations for 378,720 evolved stars and achieves a typical absolute age uncertainty of approximately 2 Gyr. By intrinsically accounting for the coverage and density of the training data, our model ensures that the resulting uncertainties reflect both the inherent noise in the data and the completeness of the sampled parameter space. Applying this method to data from the fifth-generation Sloan Digital Sky Survey Milky Way Mapper, we have produced the largest stellar age catalog for evolved stars to date.

We present age estimates for over 8100 subgiant branch (SGB) stars in Omega Centauri (ω Cen) to study its star formation history. Our large data set, which combines multi-wavelength Hubble Space Telescope photometry with MUSE metallicities, provides an unprecedented opportunity to measure individual stellar ages. We do this by fitting each star’s photometry and metallicity with theoretical isochrones that are embedded with an empirical [C + N + O]–[Fe/H] relation specific to ω Cen. The bulk of the stars have ages between 13 and 10 Gyr, with the mean stellar age being 12.08 ± 0.01 Gyr and the median age uncertainty being 0.68 Gyr. From these ages we construct the most complete age–metallicity relation for ω Cen to date. We find that the mean age of stars decreases with increasing metallicity and find two distinct streams in the age–metallicity plane, hinting at different star formation pathways. We derive an intrinsic spread in the ages of 0.75 ± 0.01 Gyr for the whole cluster, with the age spread showing a clear increase with metallicity. We verify the robustness of our age estimations by varying isochrone parameters and constraining our systematics. We find the C + N + O relation to be the most critical consideration for constraining the age–metallicity relation. We also present an SGB chromosome map with age information. In the future these stellar ages could be combined with chemical abundances to study age differences in subpopulations and uncover the chemical evolution history of this massive nuclear star cluster.

Stellar age determination for large samples of stars opens new avenues for a broad range of astronomical sciences. While precise stellar ages for evolved stars have been derived from large ground- and space-based stellar surveys, reliable age determination for cool main-sequence dwarf stars remains a challenge. In this work, we set out to estimate the age of dwarf stars from the LAMOST spectra with a data-driven approach. We build a training set by using wide binaries for which the primary component has a reliable isochrone age estimate and thus gives the age of the secondary. This training set is further supplemented with field stars and cluster stars whose ages are known. We then train a data-driven model to infer age from their spectra with the XGBoost algorithm. Given a spectral signal-to-noise ratio greater than 50, the age estimation is precise to 10%–25% for K-type stars, as younger stars have larger relative errors. Validations suggest that the underlying information used for our age estimation is largely attributed to the LAMOST spectral features of chemical abundances. This means our result is a manifestation of stellar chemical clock effectively acting on LAMOST spectra (R ≃ 1800). Applying our model to the LAMOST DR10 yields a massive age catalog for ∼4 million dwarf stars. Statistical properties, such as the age distribution, age–abundance, and age–stellar activity relations of the sample stars are discussed. The catalog is publicly accessible and can be helpful for extensive sciences from detection and characterization of Earthlike planets to Galactic archaeology.

Stellar age measurements are fundamental to understanding a wide range of astronomical processes, including Galactic dynamics, stellar evolution, and planetary system formation. However, extracting age information from main-sequence stars is complicated, with techniques often relying on age proxies in the absence of direct measurements. The Gaia data releases have enabled detailed studies of the dynamical properties of stars within the Milky Way, offering new opportunities to understand the relationship between stellar age and dynamics. In this study, we leverage high-precision astrometric data from Gaia DR3 to construct a stellar age prediction model based only on stellar dynamical properties, namely the vertical action. We calibrate two distinct, hierarchical stellar age–vertical action relations, first employing asteroseismic ages for red-giant-branch stars, then isochrone ages for main-sequence turn-off stars. We describe a framework called zoomies based on this calibration, by which we can infer ages for any star given its vertical action. This tool is open-source and intended for community use. We compare dynamical age estimates from zoomies with age measurements from open clusters and asteroseismology. We use zoomies to generate and compare dynamical age estimates for stars from the Kepler, K2, and TESS exoplanet transit surveys. While dynamical age relations are associated with large uncertainty, they are generally mass independent and depend on homogeneously measured astrometric data. These age predictions are uniquely useful for large-scale demographic investigations, especially in disentangling the relationship between planet occurrence, metallicity, and age for low-mass stars.