Prediction of Solar Eruptions with Machine-Learning Algorithms Combining Physical Models and Observations

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Space-weather prediction has been around for many decades but is reaching new heights due to the advent of more data, sophisticated data processing techniques, and computing power. Modern satellites and ground-based telescopes designed to study space weather take more data than ever before. The Solar Dynamics Observatory (SDO), for example, acquires ~1.5 terabytes of data a day. As such, the time may be right to give an affirmative answer to the question: Can we harness the data revolution to effectively predict the onset of a major solar flare? The main purpose of this three-year project is to answer this question by bringing together an interdisciplinary team of computer scientists and solar physicists to analyze data taken by the SDO, Global Oscillation Network Group (GONG), and Geostationary Operational Environmental Satellite (GOES) observatories, as well as data products derived from numerical models, using machine-learning algorithms to characterize and understand which signatures indicate the imminent eruption of a solar flare. Previous research studies have attempted to predict solar flares using a subset of these components, but not all of them. Few space-weather studies have harnessed the data revolution to predict space-weather using machine learning algorithms despite the vast amount of data available. This three-year project addresses open questions in solar flare physics. First, the project aims to determine which features contribute to local and global pre-flare signatures. While interaction between active regions can trigger flaring behavior, hyper-local phenomena can also trigger flaring behavior. Second, the project team will explore how the rate of change of any given feature influences eruptive activity on the Sun. Finally, the project aims to identify the timescales on which each individual feature best predicts future flaring activity. To accomplish these goals, the project team will use the relevant features identified in these three tasks, along with interpretable machine learning algorithms, such as state-space models, to predict solar activity. Space weather prediction is a national priority, listed as a key goal of the most recent decadal survey and a prime focus of the National Space-Weather Action Plan. This three-year project aims to build an open-source, well-documented, unified, reproducible, and operational feature dataset and machine-learning model. The project team will use this dataset and machine-learning model to teach students both through summer internships and via their book entitled Statistics, Data Mining, and Machine Learning in Heliophysics. The research and EPO agenda of this project supports the Strategic Goals of the AGS Division in discovery, learning, diversity, and interdisciplinary research.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.