AI algorithm brings us closer to predicting the Northern Lights

A group of researchers has used artificial intelligence to sort through nearly one billion images of the aurora borealis – the Northern Lights – which could help researchers understand and predict the remarkable natural phenomenon in the future.

The team developed a new algorithm to sort through more than 706 million images of the aurora in the THEMIS all-sky images that were taken between 2008 and 2022. The algorithm sorted the images into six categories based on their characteristics, demonstrating the utility of the software for categorizing large-scale atmospheric datasets.

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

The team 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 study the ethereal phenomenon at scale.

There was a lot on aurora borealis 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 star’s surface, including eruptions of solar material (coronal mass ejections or CMEs) and solar flares.

These events send charged particles into space, and when these particles react with particles in Earth’s atmosphere, they cause an ethereal glow in the sky: auroras. Particles can too disrupt electronics and electrical networks on Earth and in space, but we’re just talking about the beautiful natural phenomena right now, not the merciless chaos that space weather can unleash on humanity.

“The tagged database may reveal further insight into auroral dynamics, but at a very basic level we aimed to organize the THEMIS all-sky imaging database so that the vast amount of historical data it contains can be used more efficiently by researchers and provide a large enough sample for future studies,” Johnson said.

The intensity of solar storms is difficult to predict because scientists can’t measure the solar flares they come from with accuracy until the particles are within an hour of arriving at Earth.

The team sorted the hundreds of millions of images into six categories: rainbow, diffuse, discrete, cloudy, moon, and clear/no glow. Scientists may benefit from comparing the auroras with atmospheric data from the time the aurora occurred and linking the phenomena to the solar event that ultimately caused the light show.

A better understanding of the chemical mix of solar particles and those in Earth’s atmosphere will help scientists determine which types of auroras arise from each scenario and the ability to interrogate hundreds of millions of images rapidly (compared to the speed of this work when by humans ) may be useful for aurora research.