标题： 三维流体动力学模拟中的颗粒化特征：使用贝叶斯嵌套采样框架评估背景模型性能 显示英文标题

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

摘要： 理解粒化背景信号在解释类太阳振荡器的星震学诊断中具有至关重要的意义。 文献中存在多种对对流表面表现的描述，选择不同描述会影响我们的解释。 我们的目标是评估和比较一系列3D流体动力学模拟中粒化背景模型的性能和偏好，从而扩大此类研究的模拟数量和参数空间覆盖范围。 我们通过考虑3D模拟功率密度谱中的粒化现象来采取统计方法，其中不存在观测起源的偏差或系统性误差。 为了正确对比模型的性能，我们开发了一个贝叶斯嵌套采样框架用于模型推断和比较。 该框架通过KIC 8006161（Doris）和太阳的实际恒星数据进行了扩展。 我们发现多组分模型始终优于单组分模型，每个测试的多组分模型在特定情况下都表现出优势。 这发生在没有磁活动的模拟中，因此排除了恒星斑作为第二个粒化组分的唯一来源。 与之前的一项研究类似，我们发现一个具有单一总体幅度和两个特征频率的混合模型在许多模拟中表现良好。 此外，一些模拟中观察到超出$\nu_\mathrm{max}$值的暂定第三个粒化组分，但其在观测中的潜在存在需要进一步努力验证。 研究这些模拟中的粒化特征为研究具有精确粒化模型的恒星铺平了道路。 对粒化信号的更深入理解可能会导致现有算法确定恒星参数的补充方法。 显示英文摘要

摘要： 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 $\nu_\mathrm{max}$ 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.