Causal Discovery in Photospheric Magnetic Field Time Series for Interpretable Solar Flare Prediction
Date of Award:
8-2026
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Computer Science
Committee Chair(s)
Shah Muhammed Hamdi
Committee
Shah Muhammed Hamdi
Committee
Shuhan Yuan
Committee
Soukaina Filali-Boubrahimi
Abstract
Solar flares are capable of damaging many valuable resources, including satellites, power grids, and even human lives. Being able to predict solar flares can allow for proactive measures to be taken that can prevent that damage. Many new deep learning methods for predicting solar flares have shown promise in this task, but the decisions they make are harder to explain to humans. This makes understanding why these models make mistakes difficult, which in turn makes fixing and maintaining them more challenging. We test a recent deep learning method that helps discover relationships between different measurements of the Sun as they change over time. These relationships are relatively easy for humans to understand, and we also show that the diagram of these relationships is informative for other machine learning models when predicting solar flares.
Creative Commons License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 License.
Recommended Citation
Nelson, Nathan W., "Causal Discovery in Photospheric Magnetic Field Time Series for Interpretable Solar Flare Prediction" (2026). All Graduate Theses and Dissertations, Fall 2023 to Present. 927.
https://digitalcommons.usu.edu/etd2023/927
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