Space weather events can impact satellite communications, astronaut; health, and the electric power grid. It is thus of utmost importance; that we develop efficient, reliable tools to determine when space; weather events, such as solar flares, will occur and how strong they; will be. The SWx TREC Deep Learning Laboratory has developed several; state-of-the-art machine learning projects to improve solar flare; prediction through the use of deep learning models, generative; adversarial network data augmentation, and explainable artificial; intelligence techniques. In particular, we compared two generative; adversarial networks (GANs) to super-resolve the Solar and Heliospheric; Observatory’s Michelson Doppler Imager (SOHO/MDI) magnetogram data to; match the quality of the Solar Dynamics Observatory’s Helioseismic and; Magnetic Imager (SDO/HMI) magnetogram data. We find that both GANs are; able to preserve key features of the original SOHO/MDI magnetogram data; while achieving better resolution to match the SDO/HMI data. In the; future, we will use the combined, augmented dataset in a Long Short-Term; Memory model for solar flare prediction to see if training on the; expanded dataset results in improved predictive power compared to; training on the SDO/HMI dataset alone. In addition to data augmentation,; we have used Local Interpretable Model-Agnositc Explanations (LIME) on; our existing solar flare prediction model to provide more insight into; specific predictions. This is an important step in building trust in our; model and understanding what features are driving the model’s; predictions. In this presentation, we will discuss these recent projects; as well as future work that the SWx TREC Deep Learning Laboratory will; tackle in order to advance the field of machine learning in space; weather, including: improved hardware, better visualization; capabilities, cutting edge models, software tools, and community; resources.
