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We develop multiple Deep Learning (DL) models that advance thestate-of-the-art predictions of the global auroral particle precipitation. Weuse observations from low Earth orbiting spacecraft of the electron energy fluxto develop a model that improves global nowcasts (predictions at the time ofobservation) of the accelerated particles. Multiple Machine Learning (ML)modeling approaches are compared, including a novel multi-task model, modelswith tail- and distribution-based loss functions, and a spatio-temporallysparse 2D-convolutional model. We detail the data preparation process as wellas the model development that will be illustrative for many similar time seriesglobal regression problems in space weather and across domains. Our MLimprovements are three-fold: 1) loss function engineering; 2) multi-tasklearning; and 3) transforming the task from time series prediction tospatio-temporal prediction. Notably, the ML models improve prediction of theextreme events, historically obstinate to accurate specification and indicatethat increased expressive capacity provided by ML innovation can address grandchallenges in the science of space weather.

Harnessing expressive capacity of Machine Learning modeling to represent complex coupling of Earth's auroral space weather regimes