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Despite a growing sample of precisely measured stellar rotation periods and ages, the strength of magnetic braking and the degree of departure from standard (Skumanich-like) spindown have remained persistent questions, particularly for stars more evolved than the Sun. Rotation periods can be measured for stars older than the Sun by leveraging asteroseismology, enabling models to be tested against a larger sample of old field stars. Because asteroseismic measurements of rotation do not depend on starspot modulation, they avoid potential biases introduced by the need for a stellar dynamo to drive starspot production. Using a neural network trained on a grid of stellar evolution models and a hierarchical model-fitting approach, we constrain the onset of weakened magnetic braking. We find that a sample of stars with asteroseismically-measured rotation periods and ages is consistent with models that depart from standard spindown prior to reaching the evolutionary stage of the Sun. We test our approach using neural networks trained on model grids produced by separate stellar evolution codes with differing physical assumptions and find that the choices of grid physics can influence the inferred properties of the braking law. We identify the normalized critical Rossby number ${\rm Ro}_{\rm crit}/{\rm Ro}_\odot = 0.91\pm0.03$ as the threshold for the departure from standard rotational evolution. This suggests that weakened magnetic braking poses challenges to gyrochronology for roughly half of the main sequence lifetime of sun-like stars., Comment: 26 pages, 10 figures

The radial metallicity distribution of the Milky Way's disc is an important observational constraint for models of the formation and evolution of our Galaxy. It informs our understanding of the chemical enrichment of the Galactic disc and the dynamical processes therein, particularly radial migration. We investigate how the metallicity changes with guiding radius in the thin disc using a sample of red-giant stars with robust astrometric, spectroscopic and asteroseismic parameters. Our sample contains $668$ stars with guiding radii $4$ kpc < $R_\mathrm{g}$ < $11$ kpc and asteroseismic ages covering the whole history of the thin disc with precision $\approx 25\%$. We use MCMC analysis to measure the gradient and its intrinsic spread in bins of age and construct a hierarchical Bayesian model to investigate the evolution of these parameters independently of the bins. We find a smooth evolution of the gradient from $\approx -0.07$ dex/kpc in the youngest stars to $\approx -0.04$ dex/kpc in stars older than $10$ Gyr, with no break at intermediate ages. Our results are consistent with those based on asteroseismic ages from CoRoT, with that found in Cepheid variables for stars younger than $1$ Gyr, and with open clusters for stars younger than $6$ Gyr. For older stars we find a significantly lower metallicity in our sample than in the clusters, suggesting a survival bias favouring more metal-rich clusters. We also find that the chemical evolution model of Chiappini (2009) is too metal-poor in the early stages of disc formation. Our results provide strong new constraints for the growth and enrichment of the thin disc and radial migration, which will facilitate new tests of model conditions and physics., Comment: 15 pages, 16 figures. Accepted for publication in MNRAS

The detailed modelling of stellar oscillations is a powerful approach to characterising stars. However, poor treatment of systematics in theoretical models leads to misinterpretations of stars. Here we propose a more principled statistical treatment for the systematics to be applied to fitting individual mode frequencies with a typical stellar model grid. We introduce a correlated noise model based on a Gaussian Process (GP) kernel to describe the systematics given that mode frequency systematics are expected to be highly correlated. We show that tuning the GP kernel can reproduce general features of frequency variations for changing model input physics and fundamental parameters. Fits with the correlated noise model better recover stellar parameters than traditional methods which either ignore the systematics or treat them as uncorrelated noise., Comment: 11 pages

Young $\delta$ Scuti stars have proven to be valuable asteroseismic targets but obtaining robust uncertainties on their inferred properties is challenging. We aim to quantify the random uncertainties in grid-based modelling of $\delta$ Sct stars. We apply Bayesian inference using nested sampling and a neural network emulator of stellar models, testing our method on both simulated and real stars. Based on results from simulated stars we demonstrate that our method can recover plausible posterior probability density estimates while accounting for both the random uncertainty from the observations and neural network emulation. We find that the posterior distributions of the fundamental parameters can be significantly non-Gaussian, multi-modal, and have strong covariance. We conclude that our method reliably estimates the random uncertainty in the modelling of $\delta$ Sct stars and paves the way for the investigation and quantification of the systematic uncertainty., Comment: 10 pages, 10 figures, 2 tables, published in Monthly Notices of the Royal Astronomical Society 01/09/2023

Grid-based modelling is widely used for estimating stellar parameters. However, stellar model grid is sparse because of the computational cost. This paper demonstrates an application of a machine-learning algorithm using the Gaussian Process (GP) Regression that turns a sparse model grid onto a continuous function. We train GP models to map five fundamental inputs (mass, equivalent evolutionary phase, initial metallicity, initial helium fraction, and the mixing-length parameter) to observable outputs (effective temperature, surface gravity, radius, surface metallicity, and stellar age). We test the GP predictions for the five outputs using off-grid stellar models and find no obvious systematic offsets, indicating good accuracy in predictions.As a further validation, we apply these GP models to characterise 1,000 fake stars. Inferred masses and ages determined with GP models well recover true values within one standard deviation. An important consequence of using GP-based interpolation is that stellar ages are more precise than those estimated with the original sparse grid because of the full sampling of fundamental inputs., Comment: Accepted by MNRAS

With recent advances in modelling stars using high-precision asteroseismology, the systematic effects associated with our assumptions of stellar helium abundance ($Y$) and the mixing-length theory parameter ($\alpha_\mathrm{MLT}$) are becoming more important. We apply a new method to improve the inference of stellar parameters for a sample of Kepler dwarfs and subgiants across a narrow mass range ($0.8 < M < 1.2\,\mathrm{M_\odot}$). In this method, we include a statistical treatment of $Y$ and the $\alpha_\mathrm{MLT}$. We develop a hierarchical Bayesian model to encode information about the distribution of $Y$ and $\alpha_\mathrm{MLT}$ in the population, fitting a linear helium enrichment law including an intrinsic spread around this relation and normal distribution in $\alpha_\mathrm{MLT}$. We test various levels of pooling parameters, with and without solar data as a calibrator. When including the Sun as a star, we find the gradient for the enrichment law, $\Delta Y / \Delta Z = 1.05^{+0.28}_{-0.25}$ and the mean $\alpha_\mathrm{MLT}$ in the population, $\mu_\alpha = 1.90^{+0.10}_{-0.09}$. While accounting for the uncertainty in $Y$ and $\alpha_\mathrm{MLT}$, we are still able to report statistical uncertainties of 2.5 per cent in mass, 1.2 per cent in radius, and 12 per cent in age. Our method can also be applied to larger samples which will lead to improved constraints on both the population level inference and the star-by-star fundamental parameters., Comment: 21 pages, 15 figures, 12 tables. Accepted for publication in Monthly Notices of the Royal Astronomical Society

We report the discovery of a warm sub-Saturn, TOI-257b (HD 19916b), based on data from NASA's Transiting Exoplanet Survey Satellite (TESS). The transit signal was detected by TESS and confirmed to be of planetary origin based on radial velocity observations. An analysis of the TESS photometry, the Minerva-Australis, FEROS, and HARPS radial velocities, and the asteroseismic data of the stellar oscillations reveals that TOI-257b has a mass of $M_P=0.138\pm0.023$\,$\rm{M_J}$ ($43.9\pm7.3$\,$M_{\rm \oplus}$), a radius of $R_P=0.639\pm0.013$\,$\rm{R_J}$ ($7.16\pm0.15$\,$R_{\rm \oplus}$), bulk density of $0.65^{+0.12}_{-0.11}$ (cgs), and period $18.38818^{+0.00085}_{-0.00084}$\,$\rm{days}$. TOI-257b orbits a bright ($\mathrm{V}=7.612$\,mag) somewhat evolved late F-type star with $M_*=1.390\pm0.046$\,$\rm{M_{\odot}}$, $R_*=1.888\pm0.033$\,$\rm{R_{\odot}}$, $T_{\rm eff}=6075\pm90$\,$\rm{K}$, and $v\sin{i}=11.3\pm0.5$\,km\,s$^{-1}$. Additionally, we find hints for a second non-transiting sub-Saturn mass planet on a $\sim71$\,day orbit using the radial velocity data. This system joins the ranks of a small number of exoplanet host stars ($\sim100$) that have been characterized with asteroseismology. Warm sub-Saturns are rare in the known sample of exoplanets, and thus the discovery of TOI-257b is important in the context of future work studying the formation and migration history of similar planetary systems., Comment: 24 pages, 20 figures, 6 tables. Published in MNRAS