SWx TREC Deep Learning Laboratory: Advances in Machine Learning for
Space Weather
Abstract
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.