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  • Presentation | NG13A: Machine Learning in Space Weather and Heliophysics II Poster
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  • NG13A-0368: Toward Automated Detection and Analysis of Magnetic Bright Points to Study Flaring Region Footpoints Using Machine Learning
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  • Board 0368‚ Hall EFG (Poster Hall)
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Author(s):
Olaoluwasubomi Oyewole, New Mexico State University Main Campus (First Author, Presenting Author)
Juie Shetye, New Mexico State University
Oana Vesa, Stanford University


Scientists study the Sun to better understand how solar activity affects Earth and space. One important area of study involves very small, bright spots on the Sun’s surface called magnetic bright points. These tiny structures can tell us about how energy and magnetic activity build up in the Sun, especially in areas where solar flares begin.


In this study, we developed a way to track these bright points in high-resolution images of the Sun. We looked at how they move, change shape, and behave over time. This helps us learn more about their role in bigger solar events.


Because these bright points appear in large numbers and change quickly, we are also planning to use machine learning to automatically study their behavior. This could help scientists make better predictions about solar activity and its effects on things like satellite communication and power grids on Earth.


Our work bridges traditional observation methods with modern technology to better understand the Sun’s behavior and prepare for its impacts on our daily lives.




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