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Peculiar velocities into the next generation: cosmological parameters from large surveys without bias from non-linear structure

2008/02/29 by Alexandra Abate, Sarah Bridle, L. F. A. Teodoro +4 · 2 citations
Physics and Astronomy · #Astronomy and Astrophysical Research #Cosmology and Gravitation Theories #Galaxies: Formation, Evolution, Phenomena #astro-ph

paper · pdf · doi:10.1111/j.1365-2966.2008.13637.x

Accepted for publication in MNRAS, 12 pages, 5 figures. V2 Discussion clarified, 1 figure added, improvements to the text and figures; V3 Figure 5 Plotting error corrected, SN1a contours smaller

openalex publication_date 2008/08/28 · arxiv created 2008/11/03 · arxiv updated 2009/12/01 · openalex created_date 2021/02/01 · openalex updated_date 2026/07/28

Abstract

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 σ8 with less than 3 per cent bias from non-linearities. The expected error on σ8 after marginalizing over Ωm is approximately 16 per cent.

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