  We develop multiple Deep Learning (DL) models that advance the
state-of-the-art predictions of the global auroral particle precipitation. We
use observations from low Earth orbiting spacecraft of the electron energy flux
to develop a model that improves global nowcasts (predictions at the time of
observation) of the accelerated particles. Multiple Machine Learning (ML)
modeling approaches are compared, including a novel multi-task model, models
with tail- and distribution-based loss functions, and a spatio-temporally
sparse 2D-convolutional model. We detail the data preparation process as well
as the model development that will be illustrative for many similar time series
global regression problems in space weather and across domains. Our ML
improvements are three-fold: 1) loss function engineering; 2) multi-task
learning; and 3) transforming the task from time series prediction to
spatio-temporal prediction. Notably, the ML models improve prediction of the
extreme events, historically obstinate to accurate specification and indicate
that increased expressive capacity provided by ML innovation can address grand
challenges in the science of space weather.

Machine Learning