# AIMS - A new tool for stellar parameter determinations using asteroseismic constraints

## TL;DR

AIMS is an open-source Bayesian tool that efficiently determines stellar parameters by fitting classical and asteroseismic data, validated through tests on models and comparison with other pipelines.

## Contribution

The paper introduces AIMS, a new open-source code for stellar parameter inference using a Bayesian MCMC approach with interpolation, enhancing efficiency and accuracy in asteroseismic analysis.

## Key findings

- AIMS accurately reproduces stellar parameters within known uncertainties.
- Interpolation on pre-calculated grids is effective but sensitive to physics assumptions.
- AIMS shows consistent results compared to existing pipelines.

## Abstract

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 Li\`ege 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.