aims – a new tool for stellar parameter determinations using asteroseismic constraints

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 repres...

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Vydané v:Monthly notices of the Royal Astronomical Society Ročník 484; číslo 1; s. 771 - 786
Hlavní autori: Rendle, Ben M, Buldgen, Gaël, Miglio, Andrea, Reese, Daniel, Noels, Arlette, Davies, Guy R, Campante, Tiago L, Chaplin, William J, Lund, Mikkel N, Kuszlewicz, James S, Scott, Laura J A, Scuflaire, Richard, Ball, Warrick H, Smetana, Jiri, Nsamba, Benard
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United Kingdom Oxford University Press (OUP): Policy P - Oxford Open Option A 21.03.2019
Oxford University Press
Predmet:
Asteroseismology
Astrophysics
Aérospatiale, astronomie & astrophysique
Physical, chemical, mathematical & earth Sciences
Physics
Physique, chimie, mathématiques & sciences de la terre
Space science, astronomy & astrophysics
oscillations
fundamental parameters -stars
stars
ISSN:0035-8711, 1365-2966, 1365-2966
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Popis
Shrnutí: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.
Bibliografia:scopus-id:2-s2.0-85067033378
info:eu-repo/grantAgreement/EC/H2020/772293
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Fuel Cell Technologies Office
POCI-01-0145-FEDER-007672; POCI-01-0145-FEDER-028953; POCI-01-0145-FEDER-030389
ISSN:0035-8711
1365-2966
1365-2966
DOI:10.1093/mnras/stz031