论文标题
通过反高斯方法模拟中的弱透镜峰统计数据
The Weak Lensing Peak Statistics in the Mocks by the inverse-Gaussianization Method
论文作者
论文摘要
我们应用了\ citealt {arxiv：1607.05007}中提出的反高斯化方法，以快速产生弱透镜收敛图，并研究这些模拟中的峰值统计数据，包括峰值高度计数和峰值陡度计数。我们发现，峰高和陡度的分布与模拟非常吻合。这些峰值统计信息的差异为$ \ lyssim 20 \％$，在源红移$ z_s = 1 $的地图中。此外，峰值协方差损失促使我们考虑弱透镜峰统计数据中的超级样本差异。我们提出了校正方法，以通过在这些模拟的平均值中添加散射来有效地恢复不同垃圾箱之间的（反）相关性。最后，作为应用程序的一个示例，我们采用了改进的逆陶斯化方法，上述改进以快速生成40,000个模拟，以计算功率谱和峰值统计数据之间的精确矩阵。
We apply the inverse-Gaussianization method proposed in \citealt{arXiv:1607.05007} to fast produce weak lensing convergence maps and investigate the peak statistics, including the peak height counts and peak steepness counts, in these mocks. We find that the distribution of peak height and steepness is in good agreement with the simulation. The difference is $\lesssim 20\%$ for these peak statistics in the maps at source redshift $z_s=1$. Besides, the loss of off-diagonal elements in peak covariance motivates us to consider the super sample variance in weak lensing peak statistics. We propose correction methods to effectively recover the (anti-)correlation among different bins by adding scatters in the mean value of these mocks. Finally, as an example of the application, we adopt the improved inverse-Gaussianization method with the above improvement to fast generate 40,000 mocks to calculate precision matrices between the power spectrum and peak statistics.
