EarthCube Data Capabilities: Machine Learning Enhanced Cyberinfrastructure for Understanding and Predicting the Onset of Solar Eruptions

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Space weather is a term used to describe changing environmental conditions in the solar system caused by eruptions on the Sun's surface such as solar flares. Understanding and forecasting of solar eruptions is critically important for national security and for the economy since they are known to have adverse effects on critical technology infrastructure such as satellite and power distribution networks. Solar eruptions are caused by complex dynamics of sunspots which are often called solar active regions. The goal of this research is to build data infrastructure to characterize the properties of solar active regions from 1970 to now using advanced data from ground-based observatories and satellite missions. The database and associated cyberinfrastructure, jointly to be developed by physicists and computer scientists, will utilize advanced artificial intelligence and machine learning. By using this advanced database, a better understanding of the solar active regions and how they trigger solar eruptions will be achieved. The project has significant education and training components that will involve graduate students and junior researchers. The project will build advanced computer infrastructure to characterize solar active regions (ARs) and apply machine learning tools to predict two most significant forms of solar eruptions: the solar flares and coronal mass ejections (CMEs). The project will address two key science questions: (1) Which parameters and physical processes are most important for the onset of solar eruptions? (2) What is the accuracy of using these parameters to predict solar eruptions? The work will utilize and interface with the infrastructure developed under a previous EarthCube project. It will analyze digitized and digital high-resolution data from the Big Bear Solar Observatory (BBSO) from 1970 to now, current satellite mission data, as well as legacy data for a more comprehensive archive of flares and associated ARs. Dynamic non-potentiality properties of ARs will be derived using advanced imaging and machine learning tools. Deep learning techniques will be used to trace fibril/loop structures in the solar chromosphere and corona. Combining these with coronal field extrapolation will provide novel parameters to describe non-potentiality in ARs. Two new parameters will be derived that may be critically linked to flares and CMEs: flow motions and magnetic helicity injection in flare productive ARs. Based on flare/CME properties and important parameters derived from hosting ARs, deep learning techniques will be further adapted to predict the occurrence and energy range of flares and CMEs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.