AI Algorithm Takes Us Closer to Forecasting the Northern Lights

A team of researchers used artificial intelligence to sort through nearly a billion images of the aurora borealis—the Northern Lights—that could help researchers understand and predict the remarkable natural phenomenon.

The team developed a novel algorithm to sort through more than 706 million images of the aurora borealis in the THEMIS all-sky images taken between 2008 and 2022. The algorithm sorts the images into six categories based on their characteristics, which show usefulness. Software for classifying large-scale atmospheric datasets.

“The huge dataset is a valuable resource to help researchers understand how the solar wind interacts with Earth’s magnetosphere, the protective bubble that shields us from charged particles streaming in from the Sun,” said Jeremiah Johnson, a University of New Hampshire researcher and lead author of the study, a Univ. release. “But until now, its sheer size has limited how effectively we can use that data.”

Team Research-published last month Journal of Geophysical Research: Machine Learning and Computation— describes an algorithm trained to automatically label millions of images of the aurora, potentially helping scientists explore the ethereal phenomenon with speed at scale.

has been a lot of Auroras this yearPartly because the Sun is at the peak of its solar cycle. The peak of the Sun’s 11-year solar cycle is defined by increased activity on the surface of the star, including eruptions of solar material (coronal mass ejections, or CMEs), and solar flares.

These events send charged particles into space, and when those particles react with particles in Earth’s atmosphere, they create an ethereal glow in the sky: auroras. Particles can Disrupting electronics And power grid On Earth and in space, but we’re just talking about beautiful natural phenomena now, not the merciless chaos that space weather can rain down on mankind.

“The labeled database may reveal more insight into auroral dynamics, but at a very basic level, we aim 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 an information base for future studies. A large enough sample,” Johnson said.

Solar storms intensify It is difficult to predict Because scientists can’t accurately measure solar flares until the particles arrive within an hour of reaching Earth.

The team sorted millions of images into six categories: arc, diffuse, isolated, cloudy, moon, and clear/no aurora. Scientists can stand to gain by comparing the aurora with atmospheric data from the time the aurora occurred and linking the phenomenon to the solar event that ultimately caused the light display.

A better understanding of the chemical mix of solar particles and Earth’s atmosphere will help scientists determine what type of aurora each scenario produces, and the ability to quickly interrogate millions of images (compared to the rate of work done by humans) will be a boon to aurora research. could