2021MNRAS.501.2512L

The granulation background seen in the power spectrum of a solar-like oscillator poses a serious challenge for extracting precise and detailed information about the stellar oscillations. Using a 3D hydrodynamical simulation of the Sun computed with CO⁵bold, we investigate various background models to infer, using a Bayesian methodology, which one provides the best fit to the background in the simulated power spectrum. We find that the best fit is provided by an expression including the overall power level and two characteristic frequencies, one with an exponent of two and one with a free exponent taking on a value around six. We assess the impact of the 3D hydro-code on this result by repeating the analysis with a simulation from S TAGGER and find that the main conclusion is unchanged. However, the details of the resulting best fits differ slightly between the two codes, but we explain this difference by studying the effect of the spatial resolution and the duration of the simulation on the fit. Additionally, we look into the impact of adding white noise to the simulated time series as a simple way to mimic a real star. We find that, as long as the noise level is not too low, the results are consistent with the no-noise case.