A graph model for the clustering of dark matter halos

TL;DR

This paper applies network theory to analyze the hierarchical clustering of dark matter halos, revealing scale-free properties and proposing a new graph model that captures their complex formation dynamics.

Contribution

It introduces a novel graph model with preferential attachment that accurately reproduces the topological features of simulated dark matter halo systems.

Findings

Halo graphs exhibit a power-law degree distribution with exponent -2.

The proposed model captures the effects of mergers and tidal stripping.

Structural properties of simulated halos are effectively reproduced by the model.

Abstract

We use network theory to study topological features in the hierarchical clustering of dark matter halos. We use public halo catalogs from cosmological N-body simulations and construct tree graphs that connect halos within main halo systems. Our analysis shows these graphs exhibit a power-law degree distribution with an exponent of $-2$, and possess scale-free and self-similar properties according to the criteria of graph metrics. We propose a random graph model with preferential attachment kernels, which effectively incorporate the effects of minor mergers, major mergers, and tidal stripping. The model reproduces the structural, topological properties of simulated halo systems, providing a new way of modeling complex gravitational dynamics of structure formation.