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

TL;DR

This paper presents a method to accurately estimate the cosmological parameter sigma_8 from peculiar velocity surveys by averaging velocities in grid cells, effectively reducing nonlinear bias and maintaining precision.

Contribution

It introduces a practical averaging technique in grid cells to mitigate nonlinear effects in velocity data, comparable to principal component methods, for unbiased cosmological parameter estimation.

Findings

Averaging velocities reduces nonlinear bias without increasing errors significantly.

The method achieves less than 3% bias in sigma_8 estimation from surveys like 6dFGSv.

Estimated error on sigma_8 is approximately 16% after marginalizing over other parameters.

Abstract

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.