The Mass and Clustering of Dynamical Halos

Abstract

The halo model has been the standard framework to study the large-scale structure of the Universe over the last two decades. With the availability of increasingly high-resolution dark matter simulations and observational galaxy surveys, the current halo model is not flexible enough to incorporate higher precision constraints in a simple manner. We propose a rethinking of the conventional halo definitions to one that is both accurate to current numerical simulations and physically intuitive. This dissertation presents my work towards building an improved and interpretable halo definition. First, I show how we can use the dynamical structure of dark matter halos to motivate a physical mass definition. This new paradigm is known as dynamical halos, which are defined as the collection of orbiting particles. Then, I study the dynamical halo's clustering statistics, in which I demonstrate how they can be accurately (percent level) described with simple, and easy to interpret, models. I show that these models are highly accurate across all scales without introducing ad hoc corrections, even for traditional halo definitions. Finally, I detail the development of Oasis, a code and algorithm to generate dynamical halo catalogs for any numerical simulation.