Iterative mean-field approach to the spherical collapse of dark matter halos

TL;DR

This paper introduces an iterative mean-field method to analyze the gravitational collapse of dark matter overdensities, revealing universal density profiles of dark matter halos through a novel, versatile approach that bridges simulations and analytical solutions.

Contribution

The paper presents a new iterative mean-field approach for solving gravitational collapse dynamics, offering a unified framework that captures universal features of dark matter halos.

Findings

Successfully computes the evolution of dark matter halos.

Reveals universal density profiles independent of initial conditions.

Provides insights into intermediate asymptotic states in gravitational systems.

Abstract

Gravitational collapse of dark matter overdensities leads to the formation of dark matter halos which embed galaxies and galaxy clusters. An intriguing feature of dark matter halos is that their density profiles closely follow a universal form irrespective of the initial condition or the corresponding growth history. This represents a class of dynamical systems with emergent universalities. We propose an "iterative mean-field approach" to compute the solutions of the gravitational collapse dynamics. This approach iteratively searches for the evolution of the interaction field $\phi(t)$ -- in this case the enclosed mass profile $M(r,t)$ -- that is consistent with the dynamics, thus that $\phi(t)$ is the fix-point of the iterative mapping, $\mathcal{H}(\phi) = \phi$. The formalism replaces the N-body interactions with one-body interactions with the coarse-grained interaction field, and thus shares the spirit of the mean-field theory in statistical physics. This "iterative mean-field approach" combines the versatility of numerical simulations and the comprehensiveness of analytical solutions, and is particularly powerful in searching for and understanding intermediate asymptotic states in a wide range of dynamical systems where the solutions can not be obtained through the traditional self-similar analysis.