A team of researchers has used artificial intelligence to sort through nearly a billion images of the aurora borealis—the Northern Lights—which will help researchers understand and predict the rare natural phenomenon down the line. .

The team developed a novel algorithm to count more than 706 million images of the aurora borealis in THEMIS all-sky images taken between 2008 and 2022. the software for classifying large-scale atmospheric data.

“The large dataset is an invaluable resource that will 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 researcher at the University of New Hampshire and the lead author of the study, in a university release. “But until now, that size has limited how effectively we can use that data.”

The team’s research—PUBLISHED last month in Journal of Geophysical Research: Machine Learning and Computation—describes an algorithm trained to automatically label hundreds of millions of aurora images, potentially helping scientists explore the ethereal phenomenon at scale.

There is already many on Aurora IT 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 star’s surface, including explosions of solar material (coronal mass ejections, or CMEs), and solar flares.

These events send charged particles into space, and when the particles react with particles in the Earth’s atmosphere, they cause an ethereal light in the sky: auroras. Particles can too interfere with electronics and power grids on Earth and in space, but we are only talking about the beautiful natural phenomena of today, not the merciless chaos that space weather will rain on humanity.

“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 large amount of historical data it contains can be used is more effective for researchers and will provide a large sample for future studies,” Johnson said.

The intensity of solar storms is hard to predict because scientists can’t measure the sun’s emissions where they come from with precision until the particles are within an hour of reaching Earth.

The team sorted the hundreds of millions of images into six categories: arc, diffuse, discrete, cloudy, moon, and clear/no aurora. Scientists can benefit from comparing the aurora with atmospheric data from the time the aurora occurred and linking the phenomena to the solar phenomenon that ultimately causes the light.

A better understanding of the chemical mix of particles in the sun and the Earth’s atmosphere will help scientists determine which types of aurora will emerge from each scenario, and the ability to interrogate hundreds of millions of images with haste (compared to the speed of such work when done by humans. ) will be a benefit of aurora research.