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arXiv 1306.4684astro-ph.CO
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弱透镜功率谱和双谱层析成像的宇宙学参数:包括非高斯误差

Cosmological parameters from weak lensing power spectrum and bispectrum tomography: including the non-Gaussian errors

Issha Kayo, Masahiro Takada

摘要:

我们重新审视一个真正的权力弱透镜双谱层析成像约束宇宙学参数,结合功率谱层析成像,基于Fisher信息矩阵形式主义。为了解释两点和三点水平的全部信息,我们包括了所有的功率谱和双谱信息,这些信息是从所有可用的层析红移仓、多极仓和不同的三角形配置的组合中构建的,这些组合在一个角尺度范围内(最高可达lmax=2000作为我们的基准选择)。对于参数预测,我们使用Kayo,Takada & Jain(2013)中的晕轮模型方法来建模功率谱和双谱之间的非高斯误差协方差以及互协方差,包括晕轮样本方差或非线性版本的拍频耦合。我们发现,增加双谱信息导致约60%的改善,在暗能量的品质因数相比,单独的透镜功率谱层析成像,三个红移箱层析成像和斯巴鲁型调查探测星系在典型的红移zs~1。这一改进相当于扩大了1.6个调查区域。因此,我们的研究结果表明,双谱或更一般的任何三点相关为基础的统计进行宇宙学参数的功率谱的补充信息。然而,改进是适度的相比,以前的索赔使用高斯误差假设,因此,我们的结果意味着更少的额外信息,甚至更高阶的时刻,如四点关联函数。

英文摘要:

We re-examine a genuine power of weak lensing bispectrum tomography for constraining cosmological parameters, when combined with the power spectrum tomography, based on the Fisher information matrix formalism. To account for the full information at two- and three-point levels, we include all the power spectrum and bispectrum information built from all-available combinations of tomographic redshift bins, multipole bins and different triangle configurations over a range of angular scales (up to lmax=2000 as our fiducial choice). For the parameter forecast, we use the halo model approach in Kayo, Takada & Jain (2013) to model the non-Gaussian error covariances as well as the cross-covariance between the power spectrum and the bispectrum, including the halo sample variance or the nonlinear version of beat-coupling. We find that adding the bispectrum information leads to about 60% improvement in the dark energy figure-of-merit compared to the lensing power spectrum tomography alone, for three redshift-bin tomography and a Subaru-type survey probing galaxies at typical redshift of zs~1. The improvement is equivalent to a 1.6 larger survey area. Thus our results show that the bispectrum or more generally any three-point correlation based statistics carries complementary information on cosmological parameters to the power spectrum. However, the improvement is modest compared to the previous claim derived using the Gaussian error assumption, and therefore our results imply less additional information in even higher-order moments such as the four-point correlation function.

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