Gaussianizing the non-Gaussian lensing convergence field II. The applicability to noisy data
Gaussianizing the non-Gaussian lensing convergence field II. The applicability to noisy data
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DOI：
10.1103/physrevd.86.023515
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发表时间：
2012-01
影响因子：
5
通讯作者：
Yu Yu-Yu;Pengjie Zhang;Weipeng Lin;W. Cui;J. Fry
中科院分区：
文献类型：
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作者：
Yu Yu-Yu;Pengjie Zhang;Weipeng Lin;W. Cui;J. Fry
在论文 I（Yu 等人[1]）中，我们通过 N 体模拟表明，局部单调高斯变换可以显着降低无噪声透镜收敛场中的非高斯性。这使得高斯化成为理解高阶透镜统计的有前途的理论工具。在这里，我们研究了它在透镜数据分析中的适用性，特别是当形状测量噪声出现在透镜收敛图中时。 （i）我们发现形状测量噪声显着降低了高斯化性能，并且对于较浅的调查，这种降低会增加。 (ii)维纳滤波器可以有效地减少形状测量噪声的影响。维纳滤波透镜图的高斯化能够将偏度、峰度以及五阶和六阶累积量抑制为 10 倍或更多。它还可以有效地将双谱减少到零。
In paper I (Yu et al. [1]), we showed through N-body simulation that a local monotonic Gaussian transformation can significantly reduce non-Gaussianity in a noise-free lensing convergence field. This makes the Gaussianization a promising theoretical tool to understand high-order lensing statistics. Here we present a study of its applicability in lensing data analysis, in particular when shape measurement noise is presented in lensing convergence maps. (i) We find that shape measurement noise significantly degrades the Gaussianization performance and the degradation increases for shallower surveys. (ii) The Wiener filter is efficient in reducing the impact of shape measurement noise. The Gaussianization of the Wiener-filtered lensing maps is able to suppress skewness, kurtosis, and the 5th- and 6th-order cumulants by a factor of 10 or more. It also works efficiently to reduce the bispectrum to zero.