Characterising cosmic inhomogeneity with anomalous diffusion

TL;DR

This paper investigates the scale at which dark matter clustering transitions to homogeneity using fractal analysis, anomalous diffusion, and counts-in-cells methods applied to large cosmological simulations, revealing different characteristic scales.

Contribution

It introduces and compares anomalous diffusion methods with traditional counts-in-cells techniques for identifying cosmic homogeneity scales.

Findings

Counts-in-cells yields a homogeneity scale of ~150 Mpc/h.

Anomalous diffusion indicates a transition at about 250 Mpc/h.

Anomalous diffusion methods are sensitive to phase information in the data.

Abstract

Dark matter (DM) clustering at the present epoch is investigated from a fractal viewpoint in order to determine the scale where the self-similar scaling property of the DM halo distribution transits to homogeneity. Methods based on well-established `counts-in-cells' as well as new methods based on anomalous diffusion and random walks are investigated. Both are applied to DM halos of the biggest N-Body simulation in the `Dark Sky Simulations' (DS) catalogue and an equivalent randomly distributed catalogue. Results based on the smaller `Millennium Run' (MR) simulation are revisited and improved. It is found that the MR simulation volume is too small and prone to bias to reliably identify the onset of homogeneity. Transition to homogeneity is defined when the fractal dimension of the clustered and random distributions cannot be distinguished within the associated uncertainties. The `counts-in-cells' method applied to the DS simulation then yields a homogeneity scale roughly consistent with previous work ($\sim 150$$h^{-1}$Mpc). The characteristic length-scale for anomalous diffusion to behave homogeneously is found to be at about 250$h^{-1}$Mpc. The behaviour of the fractal dimensions for a halo catalogue with the same two-point function as the original but with shuffled Fourier phases is investigated. The methods based on anomalous diffusion are shown to be sensitive to the phase information, whereas the `counts-in-cells' methods are not.