论文标题
教皇：一种基于人群的方法来建模天文系统的空间结构
PoPE: A population-based approach to model spatial structure of astronomical systems
论文作者
论文摘要
我们提出了一种基于人群的新型贝叶斯推论方法，以模拟一组可观察到的空间分布的平均和人口差异，从低信噪比测量值的集合分析中。该方法包括（1）使用高斯过程推断平均曲线，（2）在给定一组自变量的情况下，计算轮廓可观察物的协方差。我们的模型是计算上有效的，能够从嘈杂的测量值中推断出较大人口大小的平均概况，而无需堆叠和binning数据，也不会参数化均值轮廓的形状。我们使用暗物质，气体和恒星曲线从星系形成的流体动力学模拟中提取出我们的方法的性能。人口概况估计器（POPE）在GitHub存储库中公开可用。我们的新方法对于使用大型天文学调查来测量各种天体物理系统的空间分布和内部结构应该很有用。
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.
