Peculiar velocities into the next generation: cosmological parameters from large surveys without bias from non-linear structure Peculiar velocities into the next generation
Peculiar velocities into the next generation: cosmological parameters from large surveys without bias from non-linear structure Peculiar velocities into the next generation
复制标题
进入下一代的奇异速度：来自大型调查的宇宙学参数，没有非线性结构的偏差 进入下一代的奇异速度
DOI：
10.1111/j.1365-2966.2008.13637.x
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发表时间：
2008
影响因子：
4.8
通讯作者：
Abate A
中科院分区：
文献类型：
--
作者：
Abate A
利用六度场星系速度测量（6dFGSv）的特殊速度数据，研究了质量密度波动功率谱（σ8）归一化的最佳估计方法。我们关注两个潜在的问题：(i)非线性结构增长的偏差和（ii）调查中大量的速度。通过ΛCDM-like模型的模拟来验证这些方法。我们从网格单元中平均速度的完整协方差矩阵计算可能性。这同时减少了数据点的数量，并平滑了在小尺度上占主导地位的非线性。我们展示了如何以实际的方式在预测中考虑平均，并展示了选择细胞大小的影响。我们发现，在不显著增加宇宙学参数误差条的情况下，可以选择一个显著降低非线性的单元大小。我们将我们的结果与Watkins等人和Feldman等人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力矩。我们得出的结论是，网格单元的平均性能同样好。我们发现，对于像6dFGSv这样的调查，我们可以用小于3%的非线性偏差来估计σ8。在Ωmis上边缘化后，σ8的预期误差约为16%。
We investigate methods to best estimate the normalization of the mass density fluctuation power spectrum (σ8) 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 non-linear growth of structure and (ii) the large number of velocities in the survey. Simulations of ΛCDM-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 smoothes out non-linearities 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 non-linearities without significantly increasing the error bars on cosmological parameters. We compare our results with those from a principal components analysis following Watkins et al. and Feldman et al. to select a set of optimal moments constructed from linear combinations of the peculiar velocities that are least sensitive to the non-linear scales. We conclude that averaging in grid cells performs equally well. We find that for a survey such as 6dFGSv we can estimate σ8with less than 3 per cent bias from non-linearities. The expected error on σ8after marginalizing over Ωmis approximately 16 per cent.