The signature of granulation in a solar power spectrum as seen with CO$^5$BOLD

TL;DR

This study uses 3D hydrodynamical simulations to analyze the granulation background in solar power spectra, identifying the best background model and assessing the effects of different simulation codes and noise levels.

Contribution

It introduces a Bayesian approach to model the granulation background in solar-like oscillators using 3D simulations, comparing results across different hydrodynamical codes.

Findings

Best fit includes overall power level and two characteristic frequencies

Results are consistent across CO$^5$BOLD and Stagger simulations

Adding white noise does not significantly alter the main conclusions

Abstract

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 CO$^5$BOLD, 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 2 and one with a free exponent taking on a value around 6. We assess the impact of the 3D hydro-code on this result by repeating the analysis with a simulation from Stagger 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.