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Mikkel Nørup Lund

AIMS - a new tool for stellar parameter determinations using asteroseismic constraints

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  • Ben M. Rendle, University of Birmingham
  • ,
  • Gaël Buldgen, University of Birmingham
  • ,
  • Andrea Miglio, University of Birmingham
  • ,
  • Daniel Reese, LESIA, Observatoire de Paris, PSL Research University, CNRS, Sorbonne Universités, UPMC Univ. Paris 06, Univ. Paris Diderot, Sorbonne Paris Cité, France
  • ,
  • Arlette Noels, Université de Liège
  • ,
  • Guy R. Davies, University of Birmingham
  • ,
  • Tiago L. Campante, Universidade do Porto
  • ,
  • William J. Chaplin, University of Birmingham
  • ,
  • Mikkel N. Lund
  • James S. Kuszlewicz, Max-Planck-Institut für Sonnensystemforschung
  • ,
  • Laura J. A. Scott, Keele University
  • ,
  • Richard Scuflaire, Universitè de Liège
  • ,
  • Warrick H. Ball, University of Birmingham
  • ,
  • Jiri Smetana, University of Birmingham, Imperial College London
  • ,
  • Benard Nsamba, Universidade do Porto
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.
OriginalsprogEngelsk
TidsskriftMonthly Notices of the Royal Astronomical Society
Vol/bind484
Nummer1
Sider (fra-til)771-786
ISSN0035-8711
DOI
StatusUdgivet - mar. 2019

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