Concentration, Ellipsoidal Collapse, and the Densest Dark Matter haloes

TL;DR

This paper develops an analytical framework based on energy conservation and ellipsoidal collapse to predict the properties of the densest dark matter haloes, providing insights beyond current simulation capabilities.

Contribution

It introduces an analytical method to estimate concentration and velocity dispersion of small dark matter haloes, reducing reliance on uncertain simulation extrapolations.

Findings

Analytical predictions align with simulations for larger haloes.

Velocity dispersion of cluster haloes is robust at ~10^{14} M_sun.

Predictions are sensitive to halo profile assumptions.

Abstract

The smallest dark matter haloes are the first objects to form in the hierarchical structure formation of cold dark matter (CDM) cosmology and are expected to be the densest and most fundamental building blocks of CDM structures in our universe. Nevertheless, the physical characteristics of these haloes have stayed illusive, as they remain well beyond the current resolution of N-body simulations (at redshift zero). However, they dominate the predictions (and uncertainty) in expected dark matter annihilation signal, amongst other astrophysical observables. Using the conservation of total energy and the ellipsoidal collapse framework, we can analytically find the mean and scatter of concentration $c$ and 1-D velocity dispersion $\sigma_{\rm 1d}$ for haloes of different virial mass $M_{200}$. Both $c$ and $\sigma_{\rm 1d}/M_{200}^{1/3}$ are in good agreement with numerical results within the regime probed by simulations -- slowly decreasing functions of mass that approach constant values at large masses. In particular, the predictions for the 1-D velocity dispersion of cluster mass haloes are surprisingly robust as the inverse heat capacity of cosmological haloes crosses zero at $M_{200} \sim 10^{14} M_\odot$. However, we find that current extrapolations from simulations to smallest CDM haloes dramatically depend on the assumed profile (e.g. NFW vs. Einasto) and fitting function, which is why theoretical considerations, such as the one presented here, can significantly constrain the range of feasible predictions.