4.7 Article

STATISTICAL AND SYSTEMATIC ERRORS IN THE MEASUREMENT OF WEAK-LENSING MINKOWSKI FUNCTIONALS: APPLICATION TO THE CANADA-FRANCE-HAWAII LENSING SURVEY

期刊

ASTROPHYSICAL JOURNAL
卷 786, 期 1, 页码 -

出版社

IOP Publishing Ltd
DOI: 10.1088/0004-637X/786/1/43

关键词

gravitational lensing: weak

资金

  1. Grants-in-Aid for Scientific Research [25287050] Funding Source: KAKEN

向作者/读者索取更多资源

The measurement of cosmic shear using weak gravitational lensing is a challenging task that involves a number of complicated procedures. We study in detail the systematic errors in the measurement of weak-lensing Minkowski Functionals (MFs). Specifically, we focus on systematics associated with galaxy shape measurements, photometric redshift errors, and shear calibration correction. We first generate mock weak-lensing catalogs that directly incorporate the actual observational characteristics of the Canada-France-Hawaii Lensing Survey (CFHTLenS). We then perform a Fisher analysis using the large set of mock catalogs for various cosmological models. We find that the statistical error associated with the observational effects degrades the cosmological parameter constraints by a factor of a few. The Subaru Hyper Suprime-Cam (HSC) survey with a sky coverage of similar to 1400 deg(2) will constrain the dark energy equation of the state parameter with an error of Delta w0 similar to 0.25 by the lensing MFs alone, but biases induced by the systematics can be comparable to the 1 sigma error. We conclude that the lensing MFs are powerful statistics beyond the two-point statistics only if well-calibrated measurement of both the redshifts and the shapes of source galaxies is performed. Finally, we analyze the CFHTLenS data to explore the ability of the MFs to break degeneracies between a few cosmological parameters. Using a combined analysis of the MFs and the shear correlation function, we derive the matter density Omega(m0) = 0.256 +/-(0.054) (0.046).

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据