论文标题
宇宙学互相关的特征功能
Characteristic Functions for Cosmological Cross-Correlations
论文作者
论文摘要
我们引入了一个新型的无偏见的，互相关的估计量，用于宇宙学随机场的单点统计。单点统计是用于分析高度非高斯密度场的有用工具，而互相关提供了一种将信息组合在一起并将其与噪声和系统学分开的强大方法。我们得出了一个新的反卷积分布估计器，该估计器将这两种方法的有用属性结合到一个统计范围中。使用玩具高斯随机场和线强度映射调查的两个示例模型，我们定量证明了这些属性，并证明DDE可用于推断。该新的估计量可以应用于任何一对重叠的非高斯宇宙学观察结果，包括大规模结构，Sunyaev-Zeldovich效应，弱透镜以及许多其他效果。
We introduce a novel unbiased, cross-correlation estimator for the one-point statistics of cosmological random fields. One-point statistics are a useful tool for analysis of highly non-Gaussian density fields, while cross-correlations provide a powerful method for combining information from pairs of fields and separating them from noise and systematics. We derive a new Deconvolved Distribution Estimator that combines the useful properties of these two methods into one statistic. Using two example models of a toy Gaussian random field and a line intensity mapping survey, we demonstrate these properties quantitatively and show that the DDE can be used for inference. This new estimator can be applied to any pair of overlapping, non-Gaussian cosmological observations, including large-scale structure, the Sunyaev-Zeldovich effect, weak lensing, and many others.
