Year: 2019
Type: article
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 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.
DOI: 10.1093/mnras/stz031
Institutions: Aarhus University, University of Birmingham, Centre National de la Recherche Scientifique, Université Paris Sciences et Lettres, Observatoire de Paris, Sorbonne Paris Cité, Laboratoire d’études spatiales et d’instrumentation en astrophysique, University of Liège, Universidade do Porto, Institute of Astrophysics and Space Sciences, Max Planck Institute for Solar System Research, Keele University, Imperial College London
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
Funders: Stellar Astrophysics Centre, European Commission, National Research Foundation, Danmarks Grundforskningsfond, Science and Technology Facilities Council, European Research Council, Fundação para a Ciência e a Tecnologia, Seventh Framework Programme, Horizon 2020, European Regional Development Fund, Fuel Cell Technologies Program
Awards: G1167214205, G1297482482, G132494354, G1516346500, Exploitation of Space Data for Innovative Helio- and Asteroseismology, G1578237752, G1664827575, G1763632427, G205717340, Detecting and characterizing exoplanets around evolved stars with NASA's TESS mission, G2657530899, G265847165, G274581676, G297885364, G3207356259, G3424509515, G3426520856, G3467815775, G3696820176, G3840324753, G3891694003, G393177772, G434337789, G4524310964, G4724859719, G491858407, G496129201, G5224927334, Asteroseismic characterization of exoplanet-host stars in preparation for NASA`s TESS and ESA`s PLATO space missions, G5677667711, G5702163051, G6052252660, G6367281397, G6708207806, G6998206882, G7414062669, Instituto de Astrofísica e Ciências do Espaço, G7835355061, G7965899483, G8935584975
OpenAlex ID: W2909449901