We present a flow-based generative approach to emulate grids of stellar evolutionary models. By interpreting the input parameters and output properties of these models as multi-dimensional probability distributions, we train conditional normalizing flows to learn and predict the complex relationships between grid inputs and outputs in the form of conditional joint distributions. Leveraging the expressive power and versatility of these flows, we showcase their ability to emulate a variety of evolutionary tracks and isochrones across a continuous range of input parameters. In addition, we describe a simple Bayesian approach for estimating stellar parameters using these flows and demonstrate its application to asteroseismic datasets of red giants observed by the Kepler mission. By applying this approach to red giants in open clusters NGC 6791 and NGC 6819, we illustrate how large age uncertainties can arise when fitting only to global asteroseismic and spectroscopic parameters without prior information on initial helium abundances and mixing length parameter values. We also conduct inference using the flow at a large scale by determining revised estimates of masses and radii for 15,388 field red giants. These estimates show improved agreement with results from existing grid-based modelling, reveal distinct population-level features in the red clump, and suggest that the masses of Kepler red giants previously determined using the corrected asteroseismic scaling relations have been overestimated by 5-10%.

翻译：我们提出了一种基于流的生成式方法来仿真恒星演化模型网格。通过将这些模型的输入参数和输出特性解释为多维概率分布，我们训练条件归一化流来学习和预测网格输入与输出之间以条件联合分布形式存在的复杂关系。利用这些流的强大表达能力和多功能性，我们展示了它们能够在连续输入参数范围内仿真各种演化轨迹和等时线。此外，我们描述了一种使用这些流进行恒星参数估计的简单贝叶斯方法，并演示了其在开普勒任务观测的红巨星星震数据集中的应用。通过将此方法应用于疏散星团NGC 6791和NGC 6819中的红巨星，我们阐明了当仅拟合全局星震学和光谱学参数而缺乏初始氦丰度和混合长参数值的先验信息时，如何产生较大的年龄不确定性。我们还通过为15,388颗场红巨星重新确定质量和半径估计值，大规模应用该流模型进行推断。这些估计值与现有基于网格的建模结果吻合度更高，揭示了红团簇中独特的群体层面特征，并表明先前使用修正后的星震标度关系确定的开普勒红巨星质量被高估了5-10%。