Granulation signatures in 3D hydrodynamical simulations: evaluating background model performance using a Bayesian nested sampling framework

Understanding the granulation background signal is of vital importance when interpreting the asteroseismic diagnostics of solar-like oscillators. Various descriptions exist in the literature for modelling the surface manifestation of convection, the choice of which affects our interpretations. We aim to evaluate the performance of and preference for various granulation background models for a suite of 3D hydrodynamical simulations of convection across the HR diagram, thereby expanding the number of simulations and coverage of parameter space for which such studies have been made. We take a statistical approach by considering the granulation in power density spectra of 3D simulations, where no biases or systematics of observational origin are present. To properly contrast the performance of the models, we develop a Bayesian nested sampling framework for model inference and comparison. This framework was extended to real stellar data using KIC 8006161 (Doris) and the Sun. We find that multi-component models are consistently preferred over a single-component model, with each tested multi-component model demonstrating merit in specific cases. This occurs for simulations with no magnetic activity, thus ruling out stellar faculae as the sole source of the second granulation component. Like a previous study, we find that a hybrid model with a single overall amplitude and two characteristic frequencies performs well for numerous simulations. Additionally, a tentative third granulation component beyond the value of $ν_\mathrm{max}$ is seen for some simulations, but its potential presence in observations requires further efforts. Studying the granulation signatures in these simulations paves the way to studying stars with accurate granulation models. This deeper understanding of the granulation signal may lead to complementary methods to existing algorithms for determining stellar parameters.