A key aspect in the determination of stellar properties is the comparison of observational constraints with predictions from stellar models. Asteroseismic Inference on a Massive Scale (AIMS) is an ...open source code that uses Bayesian statistics and a Markov Chain Monte Carlo approach to find a representative set of models that reproduce a given set of classical and asteroseismic constraints. These models are obtained by interpolation on a pre-calculated grid, thereby increasing computational efficiency. We test the accuracy of the different operational modes within AIMS for grids of stellar models computed with the Liège stellar evolution code (main sequence and red giants) and compare the results to those from another asteroseismic analysis pipeline, PARAM. Moreover, using artificial inputs generated from models within the grid (assuming the models to be correct), we focus on the impact on the precision of the code when considering different combinations of observational constraints (individual mode frequencies, period spacings, parallaxes, photospheric constraints,...). Our tests show the absolute limitations of precision on parameter inferences using synthetic data with AIMS, and the consistency of the code with expected parameter uncertainty distributions. Interpolation testing highlights the significance of the underlying physics to the analysis performance of AIMS and provides caution as to the upper limits in parameter step size. All tests demonstrate the flexibility and capability of AIMS as an analysis tool and its potential to perform accurate ensemble analysis with current and future asteroseismic data yields.
We present the first APOKASC catalog of spectroscopic and asteroseismic data for dwarfs and subgiants. Asteroseismic data for our sample of 415 objects have been obtained by the Kepler mission in ...short (58.5 s) cadence, and light curves span from 30 up to more than 1000 days. The spectroscopic parameters are based on spectra taken as part of the Apache Point Observatory Galactic Evolution Experiment and correspond to Data Release 13 of the Sloan Digital Sky Survey. We analyze our data using two independent scales, the spectroscopic values from DR13 and those derived from SDSS griz photometry. We use the differences in our results arising from these choices as a test of systematic temperature uncertainties and find that they can lead to significant differences in the derived stellar properties. Determinations of surface gravity ( ), mean density ( ), radius (R), mass (M), and age (τ) for the whole sample have been carried out by means of (stellar) grid-based modeling. We have thoroughly assessed random and systematic error sources in the spectroscopic and asteroseismic data, as well as in the grid-based modeling determination of the stellar quantities provided in the catalog. We provide stellar properties determined for each of the two scales. The median combined (random and systematic) uncertainties are 2% (0.01 dex; ), 3.4% ( ), 2.6% (R), 5.1% (M), and 19% (τ) for the photometric scale and 2% ( ), 3.5% ( ), 2.7% (R), 6.3% (M), and 23% (τ) for the spectroscopic scale. We present comparisons with stellar quantities in the asteroseismic catalog by Chaplin et al. that highlight the importance of having metallicity measurements for determining stellar parameters accurately. Finally, we compare our results with those coming from a variety of sources, including stellar radii determined from TGAS parallaxes and asteroseismic analyses based on individual frequencies. We find a very good agreement for all inferred quantities. The latter comparison, in particular, gives strong support to the determination of stellar quantities based on global seismology, a relevant result for future missions such as TESS and PLATO.
Abstract
We update the capabilities of the open-knowledge software instrument Modules for Experiments in Stellar Astrophysics (
MESA
). The new
auto
_
diff
module implements automatic differentiation ...in
MESA
, an enabling capability that alleviates the need for hard-coded analytic expressions or finite-difference approximations. We significantly enhance the treatment of the growth and decay of convection in
MESA
with a new model for time-dependent convection, which is particularly important during late-stage nuclear burning in massive stars and electron-degenerate ignition events. We strengthen
MESA
’s implementation of the equation of state, and we quantify continued improvements to energy accounting and solver accuracy through a discussion of different energy equation features and enhancements. To improve the modeling of stars in
MESA
, we describe key updates to the treatment of stellar atmospheres, molecular opacities, Compton opacities, conductive opacities, element diffusion coefficients, and nuclear reaction rates. We introduce treatments of starspots, an important consideration for low-mass stars, and modifications for superadiabatic convection in radiation-dominated regions. We describe new approaches for increasing the efficiency of calculating monochromatic opacities and radiative levitation, and for increasing the efficiency of evolving the late stages of massive stars with a new operator-split nuclear burning mode. We close by discussing major updates to
MESA
’s software infrastructure that enhance source code development and community engagement.
Finger on the pulse of asteroseismology Ball, Warrick
Astronomy & geophysics : the journal of the Royal Astronomical Society,
04/2023, Letnik:
64, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Abstract
Warrick Ball highlights some recent discoveries in the context of the past, present and future of asteroseismology
We present a method for measuring internal stellar structure based on asteroseismology that we call "inversions for agreement." The method accounts for imprecise estimates of stellar mass and radius ...as well as the relatively limited oscillation mode sets that are available for distant stars. By construction, the results of the method are independent of stellar models. We apply this method to measure the isothermal sound speeds in the cores of the solar-type stars 16 Cyg A and B using asteroseismic data obtained from Kepler observations. We compare the asteroseismic structure that we deduce against best-fitting evolutionary models and find that the sound speeds in the cores of these stars exceed those of the models.
ABSTRACT Owing to the remarkable photometric precision of space observatories like Kepler, stellar and planetary systems beyond our own are now being characterized en masse for the first time. These ...characterizations are pivotal for endeavors such as searching for Earth-like planets and solar twins, understanding the mechanisms that govern stellar evolution, and tracing the dynamics of our Galaxy. The volume of data that is becoming available, however, brings with it the need to process this information accurately and rapidly. While existing methods can constrain fundamental stellar parameters such as ages, masses, and radii from these observations, they require substantial computational effort to do so. We develop a method based on machine learning for rapidly estimating fundamental parameters of main-sequence solar-like stars from classical and asteroseismic observations. We first demonstrate this method on a hare-and-hound exercise and then apply it to the Sun, 16 Cyg A and B, and 34 planet-hosting candidates that have been observed by the Kepler spacecraft. We find that our estimates and their associated uncertainties are comparable to the results of other methods, but with the additional benefit of being able to explore many more stellar parameters while using much less computation time. We furthermore use this method to present evidence for an empirical diffusion-mass relation. Our method is open source and freely available for the community to use.6
Abstract
Stellar models rely on a number of free parameters. High-quality observations of eclipsing binary stars observed by Kepler offer a great opportunity to calibrate model parameters for evolved ...stars. Our study focuses on six Kepler red giants with the goal of calibrating the mixing-length parameter of convection as well as the asteroseismic surface term in models. We introduce a new method to improve the identification of oscillation modes that exploits theoretical frequencies to guide the mode identification (‘peak-bagging’) stage of the data analysis. Our results indicate that the convective mixing-length parameter (α) is ≈14 per cent larger for red giants than for the Sun, in agreement with recent results from modelling the APOGEE stars. We found that the asteroseismic surface term (i.e. the frequency offset between the observed and predicted modes) correlates with stellar parameters (Teff, log g) and the mixing-length parameter. This frequency offset generally decreases as giants evolve. The two coefficients a−1 and a3 for the inverse and cubic terms that have been used to describe the surface term correction are found to correlate linearly. The effect of the surface term is also seen in the p–g mixed modes; however, established methods for correcting the effect are not able to properly correct the g-dominated modes in late evolved stars.
The advent of space-based missions like Kepler has revolutionized the study of solar-type stars, particularly through the measurement and modeling of their resonant modes of oscillation. Here we ...analyze a sample of 66 Kepler main-sequence stars showing solar-like oscillations as part of the Kepler seismic LEGACY project. We use Kepler short-cadence data, of which each star has at least 12 months, to create frequency-power spectra optimized for asteroseismology. For each star, we identify its modes of oscillation and extract parameters such as frequency, amplitude, and line width using a Bayesian Markov chain Monte Carlo "peak-bagging" approach. We report the extracted mode parameters for all 66 stars, as well as derived quantities such as frequency difference ratios, the large and small separations and the behavior of line widths with frequency and line widths at with , for which we derive parametrizations; and behavior of mode visibilities. These average properties can be applied in future peak-bagging exercises to better constrain the parameters of the stellar oscillation spectra. The frequencies and frequency ratios can tightly constrain the fundamental parameters of these solar-type stars, and mode line widths and amplitudes can test models of mode damping and excitation.
We use asteroseismic data from the Kepler satellite to determine fundamental stellar properties of the 66 main-sequence targets observed for at least one full year by the mission. We distributed tens ...of individual oscillation frequencies extracted from the time series of each star among seven modeling teams who applied different methods to determine radii, masses, and ages for all stars in the sample. Comparisons among the different results reveal a good level of agreement in all stellar properties, which is remarkable considering the variety of codes, input physics, and analysis methods employed by the different teams. Average uncertainties are of the order of ∼2% in radius, ∼4% in mass, and ∼10% in age, making this the best-characterized sample of main-sequence stars available to date. Our predicted initial abundances and mixing-length parameters are checked against inferences from chemical enrichment laws ΔY/ΔZ and predictions from 3D atmospheric simulations. We test the accuracy of the determined stellar properties by comparing them to the Sun, angular diameter measurements, Gaia parallaxes, and binary evolution, finding excellent agreement in all cases and further confirming the robustness of asteroseismically determined physical parameters of stars when individual frequencies of oscillation are available. Baptised as the Kepler dwarfs LEGACY sample, these stars are the solar-like oscillators with the best asteroseismic properties available for at least another decade. All data used in this analysis and the resulting stellar parameters are made publicly available for the community.
ABSTRACT
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 on to 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 characterize 1000 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.
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