| As the world becomes more technologically reliant, the more susceptible society as a whole is to adverse interactions with the sun. This "space weather'' can produce significant effects on modern technology, from interrupting satellite service, to causing serious damage to Earth-side power grids. These concerns have, over the past several years, prompted an out-welling of research in an attempt to understand the processes governing, and to provide a means of forecasting, space weather events. The research presented in this thesis couples to current work aimed at understanding Coronal Mass Ejections (CMEs) and their influence on the evolution of Earth's magnetic field and associated Van Allen radiation belts. To aid in the analysis of how these solar wind transients affect Earth's magnetic field, a system named Geospace/Heliosphere Observation & Simulation Tool-kit (GHOSTkit), along with its python analysis tools, GHOSTpy, has been devised to calculate the adiabatic invariants of trapped particle motion within Earth's magnetic field. These invariants aid scientists in ordering observations of the radiation belts, providing a more natural presentation of data, but can be computationally expensive to calculate. The GHOSTpy system, in the phase presented here, is aimed at providing invariant calculations based on LFM magnetic field simulation data. This research first examines an ideal dipole application to gain understanding on system performance. Following this, the challenges of applying the algorithms to gridded LFM MHD data is examined. Performance profiles are then presented, followed by a real-world application of the system. |
