The study, recently featured in the Journal of Geophysical Research, utilized advanced artificial intelligence and machine learning tools to process over 706 million auroral images from NASA's Time History of Events and Macroscale Interactions during Substorms (THEMIS) mission. THEMIS, comprising twin spacecraft, captures images of the night sky every three seconds from 23 stations across North America, providing an unparalleled view of auroral activity.
"The massive data set is a valuable resource that can help researchers understand how the solar wind interacts with the Earth's magnetosphere, the protective bubble that shields us from charged particles streaming from the sun," said Jeremiah Johnson, associate professor of applied engineering and sciences and the study's lead author. "But until now, its huge size limited how effectively we can use that data."
To address this challenge, the researchers developed an innovative algorithm to efficiently sort and label THEMIS all-sky images (ASI) collected between 2008 and 2022. The algorithm categorized images into six distinct groups: arc, diffuse, discrete, cloudy, moon, and clear/no aurora. This systematic annotation makes it easier for scientists to filter, sort, and analyze the data.
"The labeled database could reveal further insight into auroral dynamics but at a very basic level, we aimed to organize the THEMIS all-sky image database so that the vast amount of historical data it contains can be used more effectively by researchers and provide a large enough sample for future studies," said Johnson.
The effort not only organizes a previously unwieldy dataset but also provides researchers with tools to unlock new insights into how auroral phenomena evolve over time and respond to solar events.
Research Report:Automatic Detection and Classification of Aurora in THEMIS All-Sky Images
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