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arXiv 0802.1935astro-ph
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进入下一代的特殊速度:来自大型巡天的宇宙学参数,无非线性结构偏差

Peculiar Velocities into the Next Generation: Cosmological Parameters From Large Surveys without Bias from Nonlinear Structure

Alexandra Abate, Sarah Bridle, Luis F. A. Teodoro, Michael S. Warren, Martin Hendry

摘要:

我们调查的方法,以最好的估计归一化的质量密度波动功率谱(sigma_8)使用特殊的速度数据从一个调查,如六度场星系速度调查(6dFGSv)。我们专注于两个潜在的问题(i)偏差从非线性增长的结构和(ii)大量的速度在调查中。LambdaCDM类模型的模拟被用来测试的方法。我们计算的可能性,从一个完整的协方差矩阵的速度平均在网格单元。这同时减少了数据点的数量,并消除了在小尺度上占主导地位的非线性。我们展示了如何平均可以考虑在预测中以一种实际的方式,并显示的效果的选择的细胞大小。我们发现,可以选择一个细胞的大小,显着降低非线性,而不会显着增加宇宙学参数的误差条。我们将我们的结果与Watkins等人(2002)和Feldman等人(2003)的主成分分析结果进行比较,以选择一组由对非线性尺度最不敏感的特殊速度的线性组合构建的最佳矩。我们的结论是,平均在网格单元执行同样好。我们发现,对于一个调查,如6dFGSv,我们可以估计sigma_8与小于3%的非线性偏差。在对Omega_m进行边缘化之后,sigma_8上的预期误差约为16%。

英文摘要:

We investigate methods to best estimate the normalisation of the mass density fluctuation power spectrum (sigma_8) using peculiar velocity data from a survey like the Six degree Field Galaxy Velocity Survey (6dFGSv). We focus on two potential problems (i) biases from nonlinear growth of structure and (ii) the large number of velocities in the survey. Simulations of LambdaCDM-like models are used to test the methods. We calculate the likelihood from a full covariance matrix of velocities averaged in grid cells. This simultaneously reduces the number of data points and smooths out nonlinearities which tend to dominate on small scales. We show how the averaging can be taken into account in the predictions in a practical way, and show the effect of the choice of cell size. We find that a cell size can be chosen that significantly reduces the nonlinearities without significantly increasing the error bars on cosmological parameters. We compare our results with those from a principal components analysis following Watkins et al (2002) and Feldman et al (2003) to select a set of optimal moments constructed from linear combinations of the peculiar velocities that are least sensitive to the nonlinear scales. We conclude that averaging in grid cells performs equally well. We find that for a survey such as 6dFGSv we can estimate sigma_8 with less than 3% bias from nonlinearities. The expected error on sigma_8 after marginalising over Omega_m is approximately 16 percent.

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