Abstract
The goal of aims (Asteroseismic Inference on a Massive Scale) is to estimate stellar parameters and credible intervals/error bars in a Bayesian manner from a set of asteroseismic frequency data and so-called classical constraints. To achieve reliable parameter estimates and computational efficiency, it searches through a grid of pre-computed models using an MCMC algorithm—interpolation within the grid of models is performed by first tessellating the grid using a Delaunay triangulation and then doing a linear barycentric interpolation on matching simplexes. Inputs for the modelling consist of individual frequencies from peak-bagging, which can be complemented with classical spectroscopic constraints. aims is mostly written in Python with a modular structure to facilitate contributions from the community. Only a few computationally intensive parts have been rewritten in Fortran in order to speed up calculations.
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Notes
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In some cases, this problem can further be compounded by the use of parallelisation, which is activated by setting parallel=True in AIMS_configure.py.
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The user should be careful not to choose a set of asteroseismic parameters which are redundant, as this would lead to a singular covariance matrix and poor numerical results.
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We note that this is not the preferred way of supplying Δν, as it does not correctly take into account correlations with other asteroseismic constraints. A better approach is to introduce the large separation via the seismic_constraints variable in AIMS_configure.py.
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The names “Xs” and “Zs” should be used for the surface hydrogen and metallicity content, as some of the functions in aims specifically look for these variables.
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Acknowledgements
aims is a software for fitting stellar pulsation data, developed in the context of the SPACEINN network, funded by the European Commission’s Seventh Framework Programme. DRR wishes to thank all those who helped him in the development of aims, including D. Bossini, T.L. Campante, W.J. Chaplin, H.R. Coelho, G.R. Davies, B.D.C.P. Herbert, J.S. Kuszlewicz, M.W. Long, M.N. Lund, and A. Miglio.
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Lund, M.N., Reese, D.R. (2018). Tutorial: Asteroseismic Stellar Modelling with AIMS. In: Campante, T., Santos, N., Monteiro, M. (eds) Asteroseismology and Exoplanets: Listening to the Stars and Searching for New Worlds. Astrophysics and Space Science Proceedings, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-319-59315-9_8
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