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arXiv 2210.14902astro-ph.CO
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用于宇宙学互相关的特征函数

Characteristic Functions for Cosmological Cross-Correlations

Patrick C. Breysse, Dongwoo T. Chung, Håvard T. Ihle

AI总结:

本文提出无偏的去卷积分布估计量 DDE,将单点统计与互相关结合,用于重叠非高斯宇宙学场的信息提取与推断。

摘要:

我们引入了一种新颖的、无偏的互相关估计量,用于宇宙学随机场的单点统计。单点统计是分析高度非高斯密度场的有用工具,而互相关则提供了一种强大的方法,可用于结合来自成对场的信息,并将这些信息与噪声和系统误差分离。我们推导了一种新的去卷积分布估计量(Deconvolved Distribution Estimator,DDE),将这两种方法的有用性质结合到一个统计量中。通过使用玩具高斯随机场和线强度映射巡天这两个示例模型,我们定量展示了这些性质,并表明 DDE 可用于推断。这种新估计量可应用于任何一对重叠的非高斯宇宙学观测,包括大尺度结构、Sunyaev-Zeldovich 效应、弱引力透镜以及许多其他观测。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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