Determination of the abundance of cosmic matter via the cell count moments of the galaxy distribution

¹ Aix Marseille Université, CNRS, Centre de Physique Théorique, UMR 7332, 13288 Marseille, France
e-mail: julien.bel.20@gmail.com
² Université de Toulon, CNRS, CPT, UMR 7332, 83957 La Garde, France
³ Institut Universitaire de France, 103 Bd. Saint-Michel, 75005 Paris, France

Received: 22 May 2013
Accepted: 15 November 2013

Abstract

We demonstrate that accurate and precise information about the matter content of the universe can be retrieved via a simple cell count analysis of the 3D spatial distribution of galaxies. A new clustering statistic, the galaxy clustering ratioη, is the key to this process. This is defined as the ratio between one- and two-point second-order moments of the smoothed galaxy density distribution. The distinguishing feature of this statistic is its universality: on large cosmic scales both galaxies (in redshift space) and mass (in real space) display the same η amplitude. This quantity, in addition, does not evolve as a function of redshift. As a consequence, the η statistic provides insight into characteristic parameters of the real-space power spectrum of mass density fluctuations without the need to specify the galaxy biasing function, neither a model for galaxy redshift distortions, nor the growing mode of density ripples. We demonstrate the method with the luminous red galaxy (LRG) sample extracted from the spectroscopic Sloan Digital Sky Survey (SDSS) data release 7 (DR7) catalogue. Taking weak (flat) priors of the curvature of the universe (Ω_k) and of the constant value of the dark energy equation of state (w), and strong (Gaussian) priors of the physical baryon density Ω_bh², of the Hubble constant H₀, and of the spectral index of primordial density perturbations n_s, we estimate the abundance of matter with a relative error of 8% (Ω_m=0.283±0.023). We expect that this approach will be instrumental in searching for evidence of new physics beyond the standard model of cosmology and in planning future redshift surveys, such as BigBOSS or EUCLID.