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Asteroseismic Stellar Modelling with AIMS
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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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MAISTEP -- a new grid-based machine learning tool for inferring stellar parameters I. Ages of giant-planet host stars
MAISTEP, a stacked ensemble of tree-based machine learning models trained on MESA stellar grids, infers stellar ages from atmospheric constraints and finds Hot Jupiter hosts are younger than Warm and Cold Jupiter hosts.
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