Proposed Project Title Atlantis AI Based Long Range Space Weather Prediction System

To date, no realizable method of predicting the occurrence or magnitude of a Solar Energetic Particle (SEP) event has been developed. There are multiple models currently available in the Community Coordinated Modeling Center (CCMC) through Goddard Space Flight Center that attempt to provide timely and accurate event propagation (e.g.MAG4, Enlil, and SEPMOD). None of these have yet achieved the goal of mid to long term forecasting, and so additional approaches need to be developed, tested, and verified to achieve this goal. The idea that a cascading event will develop through a series of sub-event signals that are not uniform in standard time, but instead distributed in an energy and frequency space, has the potential to be applied to space weather systems as well. The goal of this investigation is to determine a baseline response for a space weather natural time evaluation system, and then build upon that baseline to incorporate machine learning and artificial intelligence techniques. The use of AI/ML techniques provides additional possibilities for filtering and signal identification that could otherwise be overlooked.