Asteroseismic Stellar Modelling with AIMS

TL;DR

AIMS is a Bayesian tool that estimates stellar parameters from asteroseismic data efficiently by interpolating pre-computed models using MCMC, enabling reliable and scalable stellar modeling.

Contribution

It introduces a modular Python-based framework with a novel interpolation method for stellar modeling, enhancing efficiency and community contributions.

Findings

Effective interpolation within model grids using Delaunay triangulation.

Bayesian estimation of stellar parameters with credible intervals.

Open-source Python implementation with optimized Fortran components.

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.