标题： 一种用于从未分辨宽带光度数据推断星团特性的归一化流方法 I：与光谱能量分布拟合的比较 显示英文标题

标题： A normalizing flow approach for the inference of star cluster properties from unresolved broadband photometry I: Comparison to spectral energy distribution fitting

摘要： 从未分辨的宽波段光度数据中估计星团属性是一个具有挑战性的问题，传统上通过基于简单恒星种群(SSP)模型的光谱能量分布(SED)拟合方法来解决。 然而，由于其指数级的扩展，基于网格的方法会受到计算限制。 此外，模型中的随机潜在变量会使似然函数的计算变得难以处理。 这些限制可以通过现代生成式深度学习方法克服，这些方法提供了灵活且强大的工具，用于建模高维后验分布，并从学习的数据中快速推断。 我们提出了一种归一化流方法，用于从哈勃空间望远镜(HST)的宽波段光度数据中推断星团年龄、质量以及消光。 特别是，我们研究了网络在之前工作中分析过的推断问题上的行为。 我们使用SED建模代码CIGALE创建了一个包含$5 \times 10^6$个模拟星团的合成光度观测数据集。 随后，这个数据集被用来训练一种基于耦合的流，形式为条件可逆神经网络(cINN)，以从光度观测中预测星团年龄、质量和消光的后验概率分布。 我们对“邻近星系高角分辨率物理”(PHANGS)数据发布3目录中的星团参数进行了预测。 为了评估网络的能力，我们将结果与公开可用的PHANGS估计进行了比较，发现估计结果基本一致。 我们证明了归一化流方法可以成为推断星团参数的可行工具，并认为这种方法在潜在变量使似然计算难以处理以及需要高效密度估计的情况下尤其有用。 显示英文摘要

摘要： Estimating properties of star clusters from unresolved broadband photometry is a challenging problem that is classically tackled by spectral energy distribution (SED) fitting methods that are based on simple stellar population (SSP) models. However, because of their exponential scaling, grid-based methods suffer from computational limitations. In addition, stochastic latent variables in the model can make the computation of the likelihood function intractable. These limitations can be overcome by modern generative deep learning methods that offer flexible and powerful tools for modeling high-dimensional posterior distributions and fast inference from learned data. We present a normalizing flow approach for the inference of cluster age, mass, and reddening from Hubble Space Telescope (HST) broadband photometry. In particular, we explore our network's behavior on an inference problem that has been analyzed in previous works. We used the SED modeling code CIGALE to create a dataset of synthetic photometric observations for $5 \times 10^6$ mock star clusters. Subsequently, this data set was used to train a coupling-based flow in the form of a conditional invertible neural network (cINN) to predict posterior probability distributions for cluster age, mass, and reddening from photometric observations. We predicted cluster parameters for the 'Physics at High Angular resolution in Nearby GalaxieS' (PHANGS) Data Release 3 catalog. To evaluate the capabilities of the network, we compared our results to the publicly available PHANGS estimates and found that the estimates agree reasonably well. We demonstrate that normalizing flow methods can be a viable tool for the inference of cluster parameters, and argue that this approach is especially useful when latent variables make the computation of the likelihood intractable and in scenarios that require efficient density estimation.