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A key aspect in the determination of stellar properties is the comparison ofobservational constraints with predictions from stellar models. AsteroseismicInference on a Massive Scale (AIMS) is an open source code that uses Bayesianstatistics and a Markov Chain Monte Carlo approach to find a representative setof models that reproduce a given set of classical and asteroseismicconstraints. These models are obtained by interpolation on a pre-calculatedgrid, thereby increasing computational efficiency. We test the accuracy of thedifferent operational modes within AIMS for grids of stellar models computedwith the Li\`ege stellar evolution code (main sequence and red giants) andcompare 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 ofthe code when considering different combinations of observational constraints(individual mode frequencies, period spacings, parallaxes, photosphericconstraints,...). Our tests show the absolute limitations of precision onparameter inferences using synthetic data with AIMS, and the consistency of thecode with expected parameter uncertainty distributions. Interpolation testinghighlights the significance of the underlying physics to the analysisperformance of AIMS and provides caution as to the upper limits in parameterstep size. All tests demonstrate the flexibility and capability of AIMS as ananalysis tool and its potential to perform accurate ensemble analysis withcurrent and future asteroseismic data yields.

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