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  BESTP - An automated Bayesian modeling tool for asteroseismology

Jiang, C., & Gizon, L. (2021). BESTP - An automated Bayesian modeling tool for asteroseismology. Research in Astronomy and Astrophysics, 21(9): 226. doi:10.1088/1674-4527/21/9/226.

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 Creators:
Jiang, Chen1, Author              
Gizon, Laurent1, Author              
Affiliations:
1Department Solar and Stellar Interiors, Max Planck Institute for Solar System Research, Max Planck Society, ou_1832287              

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 MPIS_PROJECTS: Plato
 Abstract: Asteroseismic observations are crucial to constrain stellar models with precision. Bayesian Estimation of STellar Parameters (BESTP) is a tool that utilizes Bayesian statistics and nested sampling Monte Carlo algorithm to search for the stellar models that best match a given set of classical and asteroseismic constraints from observations. The computation and evaluation of models are efficiently performed in an automated and multi-threaded way. To illustrate the capabilities of BESTP, we estimate fundamental stellar properties for the Sun and the red-giant star HD 222076. In both cases, we find models that are consistent with observations. We also evaluate the improvement in the precision of stellar parameters when the oscillation frequencies of individual modes are included as constraints, compared to the case when only the large frequency separation is included. For the solar case, the uncertainties of estimated masses, radii and ages are reduced by 0.7%, 0.3% and 8% respectively. For HD 222076, they are reduced even more noticeably by 2%, 0.5% and 4.7% respectively. We also note an improvement of 10% for the age of HD 222076 when the Gaia parallax is included as a constraint compared to the case when only the large separation is included as a constraint.

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Language(s): eng - English
 Dates: 2021
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
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Title: Research in Astronomy and Astrophysics
Source Genre: Journal
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Publ. Info: Institute of Physics Publishing (IOP)
Pages: - Volume / Issue: 21 (9) Sequence Number: 226 Start / End Page: - Identifier: ISSN: 1674-4527
CoNE: https://pure.mpg.de/cone/journals/resource/1674-4527