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arXiv 1201.2669astro-ph.CO
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基于COSEBI的宇宙剪切层析成像及有效数据压缩

Cosmic Shear Tomography and Efficient Data Compression using COSEBIs

Marika Asgari, Peter Schneider, Patrick Simon

摘要:

上下文引力透镜是了解宇宙黑暗面的主要工具之一。准确,高效和有效的方法,能够提取这些信息以及其他宇宙学参数从宇宙剪切数据的需求正在不断增长。COSEBIs(Complete Orthogonal Sets of E-/B-Integrals)是最近发展起来的一种统计测度,它包含了在有限角度范围内测量的剪切关联函数中包含的完整的E-/B-模式可分离信息。目标。本工作的目的是测试这个新开发的统计高维参数空间的属性,并推广和测试剪切层析成像。方法.我们使用Fisher分析来研究COSEBI的有效性。我们展示了我们的结果的品质因数的数量,基于Fisher矩阵。结果我们发现,一个相对较小的COSEBI模式的数量总是足以饱和到最大的信息水平。这个数字总是小于“对数COSEBI”比“线性COSEBI”,也取决于红移箱的数量,宇宙学参数的数量和选择,以及调查的特点。结论. COSEBI提供了一种非常紧凑的分析宇宙剪切数据的方法,即,将数据中所有E/B模式可分离的二阶统计信息约简为少量COSEBI模式。此外,使用该方法,数据分箱中的任意性不再是问题,因为COSEBI模式是离散的。最后,少量的模式也意味着协方差及其倒数更容易获得,例如,从数值模拟,比剪切关联函数本身。

英文摘要:

Context. Gravitational lensing is one of the leading tools in understanding the dark side of the Universe. The need for accurate, efficient and effective methods which are able to extract this information along with other cosmological parameters from cosmic shear data is ever growing. COSEBIs, Complete Orthogonal Sets of E-/B-Integrals, is a recently developed statistical measure that encompasses the complete E-/B-mode separable information contained in the shear correlation functions measured on a finite angular range. Aims. The aim of the present work is to test the properties of this newly developed statistics for a higher-dimensional parameter space and to generalize and test it for shear tomography. Methods. We use Fisher analysis to study the effectiveness of COSEBIs. We show our results in terms of figure-of-merit quantities, based on Fisher matrices. Results. We find that a relatively small number of COSEBIs modes is always enough to saturate to the maximum information level. This number is always smaller for 'logarithmic COSEBIs' than for 'linear COSEBIs', and also depends on the number of redshift bins, the number and choice of cosmological parameters, as well as the survey characteristics. Conclusions. COSEBIs provide a very compact way of analyzing cosmic shear data, i.e., all the E-/B-mode separable second-order statistical information in the data is reduced to a small number of COSEBIs modes. Furthermore, with this method the arbitrariness in data binning is no longer an issue since the COSEBIs modes are discrete. Finally, the small number of modes also implies that covariances, and their inverse, are much more conveniently obtainable, e.g., from numerical simulations, than for the shear correlation functions themselves.

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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