Relating the structure of dark matter halos to their assembly and environment

Abstract

We use a large $N$-body simulation to study the relation of the structural properties of dark matter halos to their assembly history and environment. The complexity of individual halo assembly histories can be well described by a small number of principal components (PCs), which, compared to formation times, provide a more complete description of halo assembly histories and have a stronger correlation with halo structural properties. Using decision trees built with the random ensemble method, we find that about $60\%$, $10\%$, and $20\%$ of the variances in halo concentration, axis ratio, and spin, respectively, can be explained by combining four dominating predictors: the first PC of the assembly history, halo mass, and two environment parameters. Halo concentration is dominated by halo assembly. The local environment is found to be important for the axis ratio and spin but is degenerate with halo assembly. The small percentages of the variance in the axis ratio and spin that are explained by known assembly and environmental factors suggest that the variance is produced by many nuanced factors and should be modeled as such. The relations between halo intrinsic properties and environment are weak compared to their variances, with the anisotropy of the local tidal field having the strongest correlation with halo properties. Our method of dimension reduction and regression can help simplify the characterization of the halo population and clarify the degeneracy among halo properties.

22 pages, 11 figures, 2 tables. ApJ accepted. Comments are welcomed

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• Applying unsupervised learning to resolve evolutionary histories and explore the galaxy-halo connection in IllustrisTNG
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• A Conditional Abundance Matching Method of Extending Simulated Halo Merger Trees to Resolve Low-Mass Progenitors and Sub-halos
• Halo cluster shapes: Insights from simulated galaxies and ICL with prospects for weak lensing applications
• Testing a New Star Formation History Model from Principal Component Analysis to Facilitate Spectral Synthesis Modeling
• Predicting Halo Formation Time Using Machine Learning