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arXiv 2001.10765astro-ph.CO
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采用逆高斯化方法的模拟样本中的弱引力透镜峰统计

The Weak Lensing Peak Statistics in the Mocks by the inverse-Gaussianization Method

Zhao Chen, Yu Yu, Xiangkun Liu, Zuhui Fan

AI总结:

本研究将逆高斯化方法用于弱引力透镜峰统计,验证了其与模拟的吻合度,针对超样本方差提出校正方法,并生成大量模拟样本计算精度矩阵,提升了弱引力透镜统计分析效率。

摘要:

我们将文献[arXiv:1607.05007]提出的逆高斯化方法用于快速生成弱引力透镜会聚图,并研究这些模拟样本中的峰统计量,包括峰高度计数和峰陡度计数。我们发现,峰高度和陡度的分布与模拟结果吻合良好。在源红移$z_s=1$的图中,这些峰统计量的差异$\lesssim 20\\%$。此外,峰协方差中非对角元的缺失促使我们在弱引力透镜峰统计中考虑超样本方差。我们提出了校正方法,通过在这些模拟样本的均值中加入散射,有效恢复不同分箱之间的(反)相关性。最后,作为应用示例,我们采用经上述改进的逆高斯化方法快速生成40000个模拟样本,以计算功率谱与峰统计量之间的精度矩阵。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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