  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.
