Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAIMS - A new tool for stellar parameter determinations using asteroseismic constraints
65
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
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 Li`ege 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.
rate research
Read More
The goal of AIMS (Asteroseismic Inference on a Massive Scale) is to estimate stellar parameters and credible intervals/error bars in a Bayesian manner from a set of asteroseismic frequency data and so-called classical constraints. To achieve reliable parameter estimates and computational efficiency, it searches through a grid of pre-computed models using an MCMC algorithm -- interpolation within the grid of models is performed by first tessellating the grid using a Delaunay triangulation and then doing a linear barycentric interpolation on matching simplexes. Inputs for the modelling consist of individual frequencies from peak-bagging, which can be complemented with classical spectroscopic constraints. AIMS is mostly written in Python with a modular structure to facilitate contributions from the community. Only a few computationally intensive parts have been rewritten in Fortran in order to speed up calculations.
In a Bayesian context, theoretical parameters are correlated random variables. Then, the constraints on one parameter can be improved by either measuring this parameter more precisely - or by measuring the other parameters more precisely. Especially in the case of many parameters, a lengthy process of guesswork is then needed to determine the most efficient way to improve one parameters constraints. In this short article, we highlight an extremely simple analytical expression that replaces the guesswork and that facilitates a deeper understanding of optimization with interdependent parameters.
Distances from the Gaia mission will no doubt improve our understanding of stellar physics by providing an excellent constraint on the luminosity of the star. However, it is also clear that high precision stellar properties from, for example, asteroseismology, will also provide a needed input constraint in order to calibrate the methods that Gaia will use, e.g. stellar models or GSP_phot. For solar-like stars (F, G, K IV/V), asteroseismic data delivers at the least two very important quantities: (1) the average large frequency separation <Delta_nu> and (2) the frequency corresponding to the maximum of the modulated-amplitude spectrum nu_max. Both of these quantities are related directly to stellar parameters (radius and mass) and in particular their combination (gravity and density). We show how the precision in <Delta_nu>, nu_max, and atmospheric parameters T_eff and [Fe/H] affect the determination of gravity (log g) for a sample of well-known stars. We find that log g can be determined within less than 0.02 dex accuracy for our sample while considering precisions in the data expected for V<12 stars from Kepler data. We also derive masses and radii which are accurate to within 1sigma of the accepted values. This study validates the subsequent use of all of the available asteroseismic data on main sequence solar-like stars from the Kepler field (>500 IV/V stars) in order to provide a very important constraint for Gaia calibration of GSP_phot through the use of log g. We note that while we concentrate on IV/V stars, both the CoRoT and Kepler fields contain asteroseismic data on thousands of giant stars which will also provide useful calibration measures.
We present detailed parameter determinations of two chemically normal late A-type stars, HD 32115 and HD 37594, to uncover the reasons behind large discrepancies between two previous analyses of these stars performed with a semi-automatic procedure and a classical analysis. Our study is based on high resolution, high signal-to-noise spectra obtained at the McDonald Observatory. Our method is based on the simultaneous use of all available observables: multicolor photometry, pressure-sensitive magnesium lines, metallic lines and Balmer line profiles. Our final set of fundamental parameters fits, within the error bars, all available observables. It differs from the published results obtained with a semi-automatic procedure. A direct comparison between our new observational material and the spectra previously used by other authors shows that the quality of the data is not the origin of the discrepancies. As the two stars require a substantial macroturbulence velocity to fit the line profiles, we concluded that neglecting this additional broadening in the semi-automatic analysis is one origin of discrepancy. The use of FeI excitation equilibrium and of the Fe ionisation equilibrium, to derive effective temperature and surface gravity, respectively, neglecting all other indicators leads to a systematically erroneously high effective temperature. We deduce that the results obtained using only one parameter indicator might be biased and that those results need to be cautiously taken when performing further detailed analyses, such as modelling of the asteroseismic frequencies or characterising transiting exoplanets.
A full exploitation of the observations provided by the CoRoT and Kepler missions depends on our ability to complement these data with accurate effective temperatures and chemical abundances. We review in this contribution the major efforts that have been undertaken to characterise late-type, seismic targets based on spectra gathered as part of the ground-based, follow-up campaigns. A specific feature of the spectroscopic studies of these stars is that the gravity can be advantageously fixed to the more accurate value derived from the pulsation spectrum. We describe the impact that such an approach has on the estimation of Teff and [Fe/H]. The relevance of red-giant seismic targets for studies of internal mixing processes and stellar populations in our Galaxy is also briefly discussed.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
