We present a novel population-based Bayesian inference approach to model the average and population variance of spatial distribution of a set of observables from ensemble analysis of low signal-to-noise ratio measurements. The method consists of (1) inferring the average profile using Gaussian Processes and (2) computing the covariance of the profile observables given a set of independent variables. Our model is computationally efficient and capable of inferring average profiles of a large population size from noisy measurements, without stacking and binning data nor parameterizing the shape of the mean profile. We demonstrate the performance of our method using dark matter, gas and stellar profiles extracted from hydrodynamical cosmological simulations of galaxy formation. Population Profile Estimator (PoPE) is publicly available in a GitHub repository. Our new method should be useful for measuring the spatial distribution and internal structure of a variety of astrophysical systems using large astronomical surveys.

中文翻译：

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PoPE：一种基于人口的天文系统空间结构建模方法

我们提出了一种新的基于群体的贝叶斯推理方法，用于对来自低信噪比测量值的集合分析的一组观测值的空间分布的平均和总体方差进行建模。该方法包括 (1) 使用高斯过程推断平均剖面和 (2) 在给定一组自变量的情况下计算剖面可观测值的协方差。我们的模型计算效率高，能够从嘈杂的测量中推断出大量人口的平均轮廓，无需堆叠和分箱数据，也无需参数化平均轮廓的形状。我们使用从星系形成的流体动力学宇宙学模拟中提取的暗物质、气体和恒星剖面来证明我们方法的性能。人口概况估计器 (PoPE) 在 GitHub 存储库中公开可用。我们的新方法应该有助于使用大型天文调查测量各种天体物理系统的空间分布和内部结构。