First, we will describe the χaroν package, which simulates the neutrino yields from dark matter annihilation and decay. This will lead us into an analysis looking for an excess of neutrinos from the direction of the Sun using data from the IceCube Neutrino Observatory. Such an excess would be a signature of dark matter captured by scattering on solar nuclei and annihilating to Standard Model particles. In addition, we will describe using this same analysis framework to search for the predicted, but yet-unobserved flux of solar atmospheric neutrinos created when cosmic rays interact and produce meson in the thin solar atmosphere. Next, we will turn our attention to flavor physics, and discuss how new physics may manifest in the ratio of neutrino flavors at Earth. In particular, we will discuss the importance of tau neutrino identification in understanding the flavor triangle. Then we will introduce the TauRunner package which simulates the passage of the highest energy neutrinos through the arbitrary media, including previously neglected effects. This new simulation framework will then be applied to simulating ultra- high-mass dark matter in the solar core, in an attempt to evade the solar opacity limit. Finally, we will describe the simulation framework that has been developed for the Tau Air-Shower Mountain-Based Observatory. This proposed, next-generation detector in the Colca Valley of Peru could provide a tau-pure flux of neutrinos in the 1 PeV–100 PeV energy range.
Finally, we will describe the Prometheus simulation package, an open-source framework for simulating neutrino telescopes with arbitrary geometries in water and ice. For the first time, this allows for a consistent simulation framework between the global network of neutrino telescopes that is currently being constructed. Furthermore, this allows for the rapid prototyping of new reconstruction and data storage techniques with easy, cross-detector application. We provide three examples of such techniques: a machine-learning-based reconstruction capable of running faster than the trigger rate of neutrino telescopes; a machine-learning-based reconstruction of dimuon events in an ice-based detector; and a demonstration of efficiently storing event-level data from neutrino telescopes in quantum memory.