Non-Gaussianity of the Cosmic Baryon Fluid: Log-Poisson Hierarchy Model

TL;DR

This paper models the non-Gaussian, scale-covariant behavior of cosmic baryon fluid using a log-Poisson hierarchical cascade, aligning well with hydrodynamic simulation results in the nonlinear regime.

Contribution

It introduces a log-Poisson hierarchical cascade model to describe the non-Gaussian features of cosmic baryon fluid in the nonlinear, scale-free regime, validated by simulations.

Findings

The model accurately predicts hierarchical relations and intermittency.

The intermittency parameter decreases at low redshift.

The model aligns with hydrodynamic simulation results.

Abstract

In the nonlinear regime of cosmic clustering, the mass density field of the cosmic baryon fluid is highly non-Gaussian. It shows different dynamical behavior from collisionless dark matter. Nevertheless, the evolved field of baryon fluid is scale-covariant in the range from the Jeans length to a few ten h^{-1} Mpc, in which the dynamical equations and initial perturbations are scale free. We show that in the scale-free range, the non-Gaussian features of the cosmic baryon fluid, governed by the Navier-Stokes equation in an expanding universe, can be well described by a log-Poisson hierarchical cascade. The log-Poisson scheme is a random multiplicative process (RMP), which causes non-Gaussianity and intermittency even when the original field is Gaussian. The log-Poisson RMP contains two dimensionless parameters: $\beta$ for the intermittency and $\gamma$ for the most singular structure. All the predictions given by the log-Poisson RMP model, including the hierarchical relation, the order dependence of the intermittent exponent, the moments, and the scale-scale correlation, are in good agreement with the results given by hydrodynamic simulations of the standard cold dark matter model. The intermittent parameter $\beta$ decreases slightly at low redshift and indicates that the density field of baryon fluid contains more singular structures at lower redshifts. The applicability of the model is addressed.