Gaussianizing the non-Gaussian lensing convergence field: The performance of the Gaussianization
Gaussianizing the non-Gaussian lensing convergence field: The performance of the Gaussianization
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DOI：
10.1103/physrevd.84.023523
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发表时间：
2011-03
影响因子：
5
通讯作者：
Yu Yu-Yu;Pengjie Zhang;Weipeng Lin;W. Cui;James N.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;James N.Fry
受Neyrinck et al. 2009和Scherrer et al. 2010最近工作的启发，我们提出了高斯变换来高斯化非高斯透镜收敛场kappa。它逐像素地对kappa -> y进行局部单调变换，使新变量y的精细尺度一点概率分布函数成为高斯分布。我们通过n体模拟测试了整个y场是否为高斯场。(1)我们发现，相对于相应的平滑kappa场，在不同角度尺度上平滑的y场的偏度、峰度、5阶和6阶累积量，所提出的高斯化方法在数量级上抑制了非高斯性。在统计误差范围内，残差非高斯性往往与零一致。(2)高斯化显著抑制了双谱。此外，根据傅里叶空间的构型，残差在零附近散射。(3)在以z为（0,2）元为中心的300h(-1) Mpc距离区间内，可以用弱透镜层析成像重建物质密度的二维场，高斯化效果更好。(4)我们发现了所提出的高斯化的不完善性和复杂性。我们注意到y场存在弱的残余非高斯性。我们验证了广泛使用的对数变换是高斯变换的一个很好的近似。然而，我们也发现了明显的偏差。
Motivated by recent works of Neyrinck et al. 2009 and Scherrer et al. 2010, we proposed a Gaussian transformation to Gaussianize the non-Gaussian lensing convergence field kappa. It performs a local monotonic transformation kappa -> y pixel by pixel to make the fine-scale one-point probability distribution function of the new variable y Gaussian. We tested whether the whole y field is Gaussian through N-body simulations. (1) We found that the proposed Gaussianization suppresses the non-Gaussianity by orders of magnitude, in measures of the skewness, the kurtosis, the 5th- and 6th-order cumulants of the y field smoothed over various angular scales, relative to that of the corresponding smoothed kappa field. The residual non-Gaussianities are often consistent with zero within the statistical errors. (2) The Gaussianization significantly suppresses the bispectrum. Furthermore, the residual scatters about zero, depending on the configuration in the Fourier space. (3) The Gaussianization works with even better performance for the 2D fields of the matter density projected over similar to 300h(-1) Mpc distance interval centered at z is an element of (0, 2), which can be reconstructed from the weak lensing tomography. (4) We identified imperfectness and complexities of the proposed Gaussianization. We noticed weak residual non-Gaussianity in the y field. We verified the widely used logarithmic transformation as a good approximation to the Gaussian transformation. However, we also found noticeable deviations.