Campus, PAB 102/103
In this thesis defense, I discuss several computational physics developments. The context is the study of potential dark matter observables calculated from traditional cosmological N-body simulations. These particles based simulation techniques often suffer from shot noise when sampling of the density field.
Building on the phase space sheet (PSS) interpretation of Abel, Hahn and Kaehler (2012) of cold collisionless fluid, I develop a method for geometrically exact and robust volume- and point-sampling algorithms. These operate on a simplicial tessellation of a 3-manifold embedded in the 6-D phase space, such that the mass is interpolated between particles, which are interpreted as Langrangian flow tracers. This results in a smooth continuous and noise free density field that aids accurate interpretations of cosmological dark matter simulations.
I will discuss the application of these algorithmic developments to the indirect detection of dark matter (via decay and annihilation), studies of cosmic voids, the cosmic neutrino background, and simulations. I will also present recent work on extending these concepts to radiation transport with "adaptive beam tracing." This method extends ray-tracing, which follows 1-dimensional rays along their trajectories, to beam tracing, which instead volume-samples 3-D photon packets called "beams". I am excited about the general applications for these techniques, so I will end with a "wish list" of future research avenues.