A Better Way To Define Dark Matter Haloes

Abstract

The halo model is a successful framework for describing the distribution of matter in the Universe, from weak lensing observables to galaxy n-point correlation functions. However, its power to optimally extract cosmological parameters from contemporary surveys is limited by its accuracy at intermediate and smaller scales. This dissertation presents my work toward improving both the accuracy and interpretability of the halo model. First, I study the effects of halo exclusion criteria on halo statistics. I show that this choice introduces significant biases in models of the halo--matter correlation function. Then, I study the impact of the choice of halo boundary. I demonstrate that redefining the halo boundary leads to a simple yet accurate model of the halo--matter correlation function. Lastly, I show how we can use the dynamical structure of dark matter structures to motivate a more physical halo definition. I demonstrate this definition leads to simple halo statistics and easier to interpret halo models of clustering. This work will be fundamental to arrive at a complete theory of large scale structure that delivers the necessary precision required by upcoming surveys.