论文标题
$ n（z）$不确定性的分析边缘化
Analytic marginalization of $N(z)$ uncertainties in tomographic galaxy surveys
论文作者
论文摘要
我们提出了一种新的方法，可以在适用于当前和即将进行的光度星系调查的层析成像宇宙学分析中对红移分布（$ n（z）$）的不确定性进行边缘化。我们允许与一般协方差矩阵约束的最佳猜测$ n（z）$偏离，该矩阵描述了我们对红移分布的不确定性。原则上，这是数百或数千个新参数的边缘化，这些参数描述了潜在的偏差是红移和层析成像箱的函数。但是，通过线性扩展围绕基准模型的理论预测，可以通过分析进行边缘化，从而导致修改的数据协方差矩阵，从而有效地使数据向量的模式下降了对Redshift分布变化更敏感的数据向量。我们通过将其应用于Hyper Soprime-CAM首次数据发布中的Galaxy聚类测量结果来展示此方法。我们说明了如何在校准样品的样品变化方面边缘化，并在光度估计方法中存在巨大的总体系统不确定性，并探讨先验在红移分布中施加平滑度的影响。
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.
