arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2007.14989astro-ph.CO
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层析星系巡天中$N(z)$不确定性的解析边缘化

Analytic marginalization of $N(z)$ uncertainties in tomographic galaxy surveys

Boryana Hadzhiyska, David Alonso, Andrina Nicola, Anže Slosar

摘要:

我们提出了一种新的方法,边缘化的红移分布,$N(z)$,适用于当前和即将到来的光度星系调查的层析宇宙学分析的不确定性。我们允许任意偏差的最佳猜测$N(z)$由一个一般的协方差矩阵描述的不确定性,在我们的知识红移分布。原则上,这是对数百或数千个新参数的边缘化,这些新参数描述了作为红移和层析箱的函数的潜在偏差。然而,通过围绕基准模型线性扩展理论预测,可以分析地执行这种边缘化,从而产生修改的数据协方差矩阵,该修改的数据协方差矩阵有效地降低了对红移分布变化更敏感的数据向量的模式。我们展示了这种方法,将其应用到超Suprime-Cam第一次数据发布的星系聚类测量。我们说明了如何边缘化的校准样本的样本方差和一个大的一般系统的光度估计方法的不确定性,并探讨了先验的影响施加平滑的红移分布。

英文摘要:

We present a new method to marginalize over uncertainties in redshift distributions, $N(z)$, within tomographic cosmological analyses applicable to current and upcoming photometric galaxy surveys. We allow for arbitrary deviations from the best-guess $N(z)$ governed by a general covariance matrix describing the uncertainty in our knowledge of redshift distributions. In principle, this is marginalization over hundreds or thousands of new parameters describing potential deviations as a function of redshift and tomographic bin. However, by linearly expanding the theory predictions around a fiducial model, this marginalization can be performed analytically, resulting in a modified data covariance matrix that effectively downweights the modes of the data vector that are more sensitive to redshift distribution variations. We showcase this method by applying it to the galaxy clustering measurements from the Hyper Suprime-Cam first data release. We illustrate how to marginalize over sample-variance of the calibration sample and a large general systematic uncertainty in photometric estimation methods, and explore the impact of priors imposing smoothness in the redshift distributions.

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