AIMS - Asteroseismic Inference on a Massive Scale
Andrea Miglio (University of Birmingham), Ben Rendle (University of Birmingham), Laura Scott (University of Birmingham), Arlette Noels (Universite de Liege), Daniel Reese (LESIA, Paris Observatory), et al.

The comparison of observational constraints with predictions from stellar models is a key aspect in the determination of stellar properties. AIMS is a code that relies on a Monte-Carlo-Markov-Chain approach to find a representative set of models which reproduce a given set of classical and asteroseismic constraints. These models are obtained by interpolation from a pre-calculated grid thereby increasing computational efficiency. Here we test the accuracy of the different operational modes within AIMS for two grids of stellar models computed with CLES (main sequence and giants), and compare the results to stars with well constrained parameters from the literature. A series of interpolation tests to determine frequencies around numax are analysed. Moreover, by using a set of artificial data generated from models within the grid, we show the impact of considering different combination of observational constraints (individual mode frequencies, period spacing, parallaxes, photospheric constrains,…) on the precision of the inferred stellar properties.