Power Spectrum Estimation from Peculiar Velocity Catalogues

TL;DR

This paper estimates the matter power spectrum using galaxy peculiar velocities, finding agreement with LCDM on small scales and potential excess power on large scales, with implications for understanding cosmic structure.

Contribution

It introduces a method to estimate the power spectrum directly from peculiar velocity catalogues and compares it with previous approaches, highlighting the uncertainties at large scales.

Findings

Good agreement with LCDM on scales of k > 0.01 h Mpc-1

Potential excess power on scales of k < 0.01 h Mpc-1

Discrimination between linear and nonlinear clustering at small scales

Abstract

The peculiar velocities of galaxies are an inherently valuable cosmological probe, providing an unbiased estimate of the distribution of matter on scales much larger than the depth of the survey. Much research interest has been motivated by the high dipole moment of our local peculiar velocity field, which suggests a large scale excess in the matter power spectrum, and can appear to be in some tension with the LCDM model. We use a composite catalogue of 4,537 peculiar velocity measurements with a characteristic depth of 33 h-1 Mpc to estimate the matter power spectrum. We compare the constraints with this method, directly studying the full peculiar velocity catalogue, to results from Macaulay et al. (2011), studying minimum variance moments of the velocity field, as calculated by Watkins, Feldman & Hudson (2009) and Feldman, Watkins & Hudson (2010). We find good agreement with the LCDM model on scales of k > 0.01 h Mpc-1. We find an excess of power on scales of k < 0.01 h Mpc-1, although with a 1 sigma uncertainty which includes the LCDM model. We find that the uncertainty in the excess at these scales is larger than an alternative result studying only moments of the velocity field, which is due to the minimum variance weights used to calculate the moments. At small scales, we are able to clearly discriminate between linear and nonlinear clustering in simulated peculiar velocity catalogues, and find some evidence (although less clear) for linear clustering in the real peculiar velocity data.