Cosmological constraints from the redshift dependence of the volume effect using the galaxy 2-point correlation function across the line-of-sight

Abstract

We develop a methodology to use the redshift dependence of the galaxy 2-point correlation function (2pCF) across the line-of-sight, $\xi (r_{\perp })$, as a probe of cosmological parameters. The positions of galaxies in comoving Cartesian space varies under different cosmological parameter choices, inducing a {\it redshift-dependent scaling} in the galaxy distribution. This geometrical distortion can be observed as a redshift-dependent rescaling in the measured $\xi (r_{\perp })$. We test this methodology using a sample of 1.75 billion mock galaxies at redshifts 0, 0.5, 1, 1.5, 2, drawn from the Horizon Run 4 N-body simulation. The shape of $\xi (r_{\perp })$ can exhibit a significant redshift evolution when the galaxy sample is analyzed under a cosmology differing from the true, simulated one. Other contributions, including the gravitational growth of structure, galaxy bias, and the redshift space distortions, do not produce large redshift evolution in the shape. We show that one can make use of this geometrical distortion to constrain the values of cosmological parameters governing the expansion history of the universe. This method could be applicable to future large scale structure surveys, especially photometric surveys such as DES, LSST, to derive tight cosmological constraints. This work is a continuation of our previous works as a strategy to constrain cosmological parameters using redshift-invariant physical quantities.

12 pages, 6 figures, Accepted for publication in ApJ

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Cited by in corpus (4)

• Tomographic Alcock-Paczynski Test with Redshift-Space Correlation Function: Evidence for the Dark Energy Equation of State Parameter w>-1
• Cosmological Parameter Estimation from the Two-Dimensional Genus Topology -- Measuring the Expansion History using the Genus Amplitude as a Standard Ruler
• Using the Mark Weighted Correlation Functions to Improve the Constraints on Cosmological Parameters
• Deep learning for cosmological parameter inference from a dark matter halo density field