CAREER: End-to-End Active Region-based Heliospheric Forecasting System Using Multi-spacecraft Data and Machine Learning

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Solar flares are one of the most impactful solar eruptive activities that occur. They are due to magnetic reconnection, where highly fluctuating magnetic fields collapse to a lower energy state releasing energy into space. This project focuses on prediction of solar flares through using machine learning techniques on solar observations. The broader impacts include public outreach to impact students at all educational levels in Utah from middle-school to college level by organizing a yearly space-weather panel discussion on Utah Public Radio. Indigenous American student research experiences will be funded every summer. In addition, the PI, an early career woman scientist, will develop a new applied laboratory component to her applied data mining classes. The research has the potential to increase national security and US competitiveness by lessening the potential risk related to defense and space exploration missions from space weather events. Active region magnetic field parameters, extracted from solar photospheric vector magnetograms, have been routinely used to predict solar flare occurrences. Despite recent advancements in solar flare prediction, there are significant barriers to efficiently combine different space-borne instruments’ observations spanning multiple solar cycles, to train robust and unbiased solar flare models. This project is a five-year research program that aims at leveraging state-of-the-art machine learning models to discover the driving factors of extreme solar flares in different solar cycles, assess the impact of active regions’ properties on solar transient events, and transfer the learned knowledge to other models of the heliosphere. To achieve the vision, the project will develop a high-spatial resolution active regions vector magnetogram dataset, that spans two solar cycles, based on three magnetographs on-board NASA’s Solar Dynamics Observatory, Solar and Heliospheric Observatory and Hinode (Thrust 1). The new high- quality and high-resolution magnetic field maps will allow the study of small-scale active regions’ physical characteristics that were never examined in the context of solar flare prediction. The project will generate comprehensive magnetic field parameters multivariate time series (MVTS) dataset useful to both Data Science and Space Weather communities for modeling various solar phenomena (Thrust 2). Finally, the project will build an accurate and robust solar flare prediction model and use the learned predictive patterns to initialize other solar events predictive models (Thrust 3). The end goal of this CAREER proposal, is to leverage the cross-field of applied ML methods in the field of astrophysics to improve our understanding of the physical attributes of active regions that drive different types of solar flares, and enable scientists to perform comparative, reproducible, and data-driven studies on the prediction of solar flare events. One of the by-products of this research will be an unprecedented comprehensive solar flare catalog supplemented with parent active regions’ magnetic field parameters’ multivariate time series data that will be freely available through Application Programming Interface (API) for its wide potential usage (e.g., conduct statistical studies, train ML-based and physics-based models).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.