论文标题
暗物质光环聚类的图形模型
A graph model for the clustering of dark matter halos
论文作者
论文摘要
我们使用网络理论来研究暗物质光环的分层聚类中的拓扑特征。我们使用宇宙N体模拟中的公共光环目录，并使用在主要光晕系统中连接光晕的构造树图。我们的分析表明，这些图显示了一个幂律学位分布，指数为$ -2 $，并且根据图指标的标准具有无尺度和自相似的特性。我们提出了一个带有优先附着内核的随机图模型，该模型有效地结合了次要合并，主要合并和潮汐剥离的影响。该模型重现了模拟光环系统的结构，拓扑特性，从而提供了一种建模结构形成复杂重力动力学的新方法。
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.
