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arXiv 0907.0795astro-ph.CO
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一种新的宇宙剪切函数:有限区间上的优化E-/B-模分解

A new cosmic shear function: Optimised E-/B-mode decomposition on a finite interval

Liping Fu, Martin Kilbinger

摘要:

宇宙剪切场的E模和B模分解是弱引力透镜的重要诊断。然而,通常使用的技术来执行这种分离遭受模式混合非常小或非常大的规模。本文介绍了有限区间上宇宙剪切两点关联的一种新的E/B模分解。这种新的统计优化宇宙学应用,通过最大化的信噪比(S/N)和品质因数(FoM)的基础上的Fisher矩阵的宇宙学参数Omega_m和sigma_8。我们改善了S/N和FoM的结果大大相对于最近推出的环统计,这也提供了E-/B-模式分离有限的角度范围。S/N(FoM)在1到220角分之间的角标度上大三(二)倍。此外,该方法得到的结果比孔径-质量色散^[2]更好<M_ap>,信噪比(FoM)提高了20%(10%)。我们的结果依赖于调查参数,最重要的是两点剪切关联函数的协方差。虽然我们假设参数根据CFHTLS范围内的调查,我们的方法和优化方案可以很容易地应用到任何给定的调查设置和观测参数。可以定义任意量,E/B模式滤波器相对于其进行优化,因此概括了新剪切统计的目的和背景。

英文摘要:

The decomposition of the cosmic shear field into E- and B-mode is an important diagnostic in weak gravitational lensing. However, commonly used techniques to perform this separation suffer from mode-mixing on very small or very large scales. We introduce a new E-/B-mode decomposition of the cosmic shear two-point correlation on a finite interval. This new statistic is optimised for cosmological applications, by maximising the signal-to-noise ratio (S/N) and a figure of merit (FoM) based on the Fisher matrix of the cosmological parameters Omega_m and sigma_8. We improve both S/N and FoM results substantially with respect to the recently introduced ring statistic, which also provides E-/B-mode separation on a finite angular range. The S/N (FoM) is larger by a factor of three (two) on angular scales between 1 and 220 arc minutes. In addition, it yields better results than for the aperture-mass dispersion <M_ap>^2, with improvements of 20% (10%) for S/N (FoM). Our results depend on the survey parameters, most importantly on the covariance of the two-point shear correlation function. Although we assume parameters according to the CFHTLS-Wide survey, our method and optimisation scheme can be applied easily to any given survey settings and observing parameters. Arbitrary quantities, with respect to which the E-/B-mode filter is optimised, can be defined, therefore generalising the aim and context of the new shear statistic.

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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