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With the observations of an unprecedented number of oscillating subgiant stars expected from NASAs TESS mission, the asteroseismic characterization of subgiant stars will be a vital task for stellar population studies and for testing our theories of stellar evolution. To determine the fundamental properties of a large sample of subgiant stars efficiently, we developed a deep learning method that estimates distributions of fundamental parameters like age and mass over a wide range of input physics by learning from a grid of stellar models varied in eight physical parameters. We applied our method to four Kepler subgiant stars and compare our results with previously determined estimates. Our results show good agreement with previous estimates for three of them (KIC 11026764, KIC 10920273, KIC 11395018). With the ability to explore a vast range of stellar parameters, we determine that the remaining star, KIC 10005473, is likely to have an age 1 Gyr younger than its previously determined estimate. Our method also estimates the efficiency of overshooting, undershooting, and microscopic diffusion processes, from which we determined that the parameters governing such processes are generally poorly-constrained in subgiant models. We further demonstrate our methods utility for ensemble asteroseismology by characterizing a sample of 30 Kepler subgiant stars, where we find a majority of our age, mass, and radius estimates agree within uncertainties from more computationally expensive grid-based modelling techniques.
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Asteroseismic measurements enable inferences of the underlying stellar structure, such as the density and the speed of sound at various points within the interior of the star. This provides an opportunity to test stellar evolution theory by assessing whether the predicted structure of a star agrees with the measured structure. Thus far, this kind of inverse analysis has only been applied to the Sun and three solar-like main-sequence stars. Here we extend the technique to stars on the subgiant branch, and apply it to one of the best-characterized subgiants of the Kepler mission, HR 7322. The observation of mixed oscillation modes in this star facilitates inferences of the conditions of its inert helium core, nuclear-burning hydrogen shell, and the deeper parts of its radiative envelope. We find that despite significant differences in the mode frequencies, the structure near to the center of this star does not differ significantly from the predicted structure.
Models of solar-like oscillators yield acoustic modes at different frequencies than would be seen in actual stars possessing identical interior structure, due to modelling error near the surface. This asteroseismic surface term must be corrected when mode frequencies are used to infer stellar structure. Subgiants exhibit oscillations of mixed acoustic ($p$-mode) and gravity ($g$-mode) character, which defy description by the traditional $p$-mode asymptotic relation. Since nonparametric diagnostics of the surface term rely on this description, they cannot be applied to subgiants directly. In Paper I, we generalised such nonparametric methods to mixed modes, and showed that traditional surface-term corrections only account for mixed-mode coupling to, at best, first order in a perturbative expansion. Here, we apply those results, modelling subgiants using asteroseismic data. We demonstrate that, for grid-based inference of subgiant properties using individual mode frequencies, neglecting higher-order effects of mode coupling in the surface term results in significant systematic differences in the inferred stellar masses, and measurable systematics in other fundamental properties. While these systematics are smaller than those resulting from other choices of model construction, they persist for both parametric and nonparametric formulations of the surface term. This suggests that mode coupling should be fully accounted for when correcting for the surface term in seismic modelling with mixed modes, irrespective of the choice of correction used. The inferred properties of subgiants, in particular masses and ages, also depend on the choice of surface-term correction, in a different manner from both main-sequence and red giant stars.
In the asymptotic parameterisation of mode frequencies, the phase function $epsilon( u)$ completely specifies the detailed structure of the frequency eigenvalues. In practice, however, this function of frequency is reduced to a single scalar $epsilon$, defined, particularly by observers, as the intercept of a least-squares fit to the frequencies against radial order, or via the central value of this function. The procedure by which this is done is not unique. We derive a few simple expressions relating various observational estimators of $epsilon$ for radial modes to each other, and to the underlying theoretical object. In particular we demonstrate that a ``reduced functional parameterisation is both insensitive to mis-estimations of $Delta u$, and easy to evaluate locally in terms of both observational and theoretical quantities. It has been shown previously that such a local definition of $epsilon$ can distinguish between stars on the ascending part of the red giant branch and those in the red clump. We find that this sensitivity to evolutionary stage arises from differences in the local frequency derivative of the underlying phase function, a consequence of differences in internal structure. By constructing an HR-like diagram out of purely seismic observables, we provide a unified view of the textit{Kepler} asteroseismic sample, as well as the initial results from textit{TESS}. We investigate how various astrophysical quantities and modelling parameters affect the morphology of isochrones on this seismic diagram. We also show that $epsilon$ can be used as an independent input when deriving stellar parameters from global asteroseismic quantities.
Asteroseismology is well-established in astronomy as the gold standard for determining precise and accurate fundamental stellar properties like masses, radii, and ages. Several tools have been developed for asteroseismic analyses but many of them are closed-source and therefore not accessible to the general astronomy community. Here we present $texttt{pySYD}$, a Python package for detecting solar-like oscillations and measuring global asteroseismic parameters. $texttt{pySYD}$ was adapted from the IDL-based $texttt{SYD}$ pipeline, which was extensively used to measure asteroseismic parameters for $textit{Kepler}$ stars. $texttt{pySYD}$ was developed using the same well-tested methodology and comes with several new improvements to provide accessible and reproducible results. Well-documented, open-source asteroseismology software that has been benchmarked against closed-source tools are critical to ensure the reproducibility of legacy results from the $textit{Kepler}$ mission. Moreover, $texttt{pySYD}$ will also be a promising tool for the broader astronomy community to analyze current and forthcoming data from the NASA TESS mission.
It has been demonstrated that the time variability of a stars brightness at different frequencies can be used to infer its surface gravity, radius, mass, and age. With large samples of light curves now available from Kepler and K2, and upcoming surveys like TESS, we wish to quantify the overall information content of this data and identify where the information resides. As a first look into this question we ask which stellar parameters we can predict from the brightness variations in red-giant stars data and to what precision, using a data-driven model. We demonstrate that the long-cadence (30-minute) Kepler light curves for 2000 red-giant stars can be used to predict their $T_{rm eff}$ and $log g$. Our inference makes use of a data-driven model of a part of the autocorrelation function (ACF) of the light curve, where we posit a polynomial relationship between stellar parameters and the ACF pixel values. We find that this model, trained using 1000 stars, can be used to recover the temperature $T_{rm eff}$ to $<$100 K, the surface gravity to $<$ 0.1 dex, and the asteroseismic power-spectrum parameters $rm u_{max}$ and $rm Delta{ u}$ to $<11$ $mu$Hz and $<0.9$ $mu$Hz ($lesssim$ 15%). We recover $T_{rm eff}$ from range of time-lags 0.045 $<$ $T_{rm lag}$ $<$ 370 days and the $log g$, $rm u_{max}$ and $rm Delta{ u}$ from the range 0.045 $<$ $T_{rm lag}$ $<$ 35 days. We do not discover any information about stellar metallicity. The information content of the data about each parameter is empirically quantified using this method, enabling comparisons to theoretical expectations about convective granulation.
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