Context. Understanding the granulation background signal is vital when interpreting the asteroseismic diagnostics of solar-like oscillators. Various descriptions exist throughout literature for modelling the surface manifestation of convection, with the choice of description affecting our interpretations.
Aims. 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 Hertzsprung-Russell diagram, thereby expanding the number of simulations and coverage of parameter space for which such investigations have been made.
Methods. We took a statistical approach by considering the granulation signatures in power density spectra of 3D hydrodynamical simulations, in which no biases or systematics of observational origin are present. To properly contrast the performance of the background models, we developed a Bayesian nested sampling framework for model inference and comparison. This framework was subsequently extended to real stellar data using the solar analogue KIC 8006161 (Doris) and the Sun.
Results. We find that multi-component models are consistently preferred over a single-component model, with each tested multicomponent model demonstrating merit in specific cases. This occurs for simulations with no magnetic activity, ruling out stellar faculae as the sole source of the second granulation component. Similar to 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 max is seen for some simulations, but its potential presence in observations requires further study.
Conclusions. Studying the granulation signatures in these simulations paves the way for studying real stars with accurate granulation models. This deeper understanding of the granulation signal may lead to complementary methods to existing algorithms for determining stellar parameters, with the goal of providing an independent radius estimate for stars where oscillations are not observable.
The solar convective conundrum — the order-of-magnitude discrepancy between helioseismically inferred deep convective velocities and those predicted by global simulations — has resisted resolution for over a decade. Three suspects have been proposed: missing physics, insufficient resolution, and the way simulations are configured. ReCon² addresses all three together in a single controlled programme, with the third element implemented through a novel staged column-relaxation strategy: solar convection is first developed in a narrow horizontal column, where large-scale unstable modes are geometrically excluded, then progressively expanded to production widths while carrying the equilibrated small-scale physics forward.
This poster, presented at Cool Stars 23 (Tokyo, 15–19 June 2026), reports first-stage results from the cz_f25_d100 run: a 25 Mm wide column at Δx = 100 km finest resolution, with fully realistic physics (OPAL/FreeEOS equation of state with ionization, OP opacities and radiative diffusion in the interior, multi-bin short-characteristics radiative transfer at the surface, fully compressible MHD, entropy-based HLLS solver), initialized from rest.
Horizontally-averaged entropy perturbation statistics over 300 hours of simulated time show a coherent thermal mixing layer descending ~140 Mm into the convection zone. The active layer is strongly negative-skewed (≤ −2) — the statistical signature of entropy rain (Käpylä 2025), indicating distributions dominated by narrow cold extremes. A compensating positive-skew layer at the leading edge marks warm material displaced by penetrating cold plumes, and discrete late-time arcs at z ≈ 480–540 Mm correspond to individual extreme plumes reaching ~200 Mm depth, exhibiting Käpylä's predicted "tree" morphology. A complementary photospheric kinetic-energy spectrum from an exploratory wider-box run shows that the surface convective physics matches established surface-tracking diagnostics within the box-resolved range.