Graph model for the clustering of dark matter halos

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 that 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.