The BAyesian STellar algorithm (BASTA): a fitting tool for stellar studies, asteroseismology, exoplanets, and Galactic archaeology

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Authors
Aguirre Børsen-Koch, V.
Rørsted, J.L.
Justesen, A.B.
Stokholm, A
Verma, K.
Winther, M.L.
Knudstrup, E.
Nielsen, K.B.
Sahlholdt, C.
Larsen, J.R.
Advisors
Issue Date
2021-09-29
Type
Article
Preprint
Keywords
Asteroseismology , Methods: numerical , Methods: statistical , Stars: fundamental parameters
Research Projects
Organizational Units
Journal Issue
Citation
Monthly Notices of the Royal Astronomical Society, Volume 509, Issue 3, January 2022, Pages 4344–4364, https://doi.org/10.1093/mnras/stab2911
Abstract

We introduce the public version of the BAyesian STellar Algorithm (BASTA), an open-source code written in PYTHON to determine stellar properties based on a set of astrophysical observables. BASTA has been specifically designed to robustly combine large data sets that include asteroseismology, spectroscopy, photometry, and astrometry. We describe the large number of asteroseismic observations that can be fit by the code and how these can be combined with atmospheric properties (as well as parallaxes and apparent magnitudes), making it the most complete analysis pipeline available for oscillating main-sequence, subgiant, and red giant stars. BASTA relies on a set of pre-built stellar isochrones or a custom-designed library of stellar tracks, which can be further refined using our interpolation method (both along and across stellar tracks or isochrones). We perform recovery tests with simulated data that reveal levels of accuracy at the few percent level for radii, masses, and ages when individual oscillation frequencies are considered, and show that asteroseismic ages with statistical uncertainties below 10 per cent are within reach if our stellar models are reliable representations of stars. BASTA is extensively documented and includes a suite of examples to support easy adoption and further development by new users.

Table of Contents
Description
2021 The Author(s), Preprint
Publisher
Oxford University Press
Journal
Book Title
Series
Monthly Notices of the Royal Astronomical Society;2022
PubMed ID
DOI
ISSN
0035-8711
EISSN