1859, 1972, 1989, 2000, 2003, 2006 and 2022. These are years of recorded solar storms pelting the Earth, in varying degrees, and reminding earthlings of the sun’s intense power and potential for disruption. Communications systems, including telegraph networks in 1859 to Starlink satellites in 2022, were among critical equipment bearing the brunt of the sun’s wrath in each episode.
“Solar activity can cause disruptions, ranging from minor inconveniences to dangerous outages on Earth,” says Utah State University computer scientist Soukaina Filali Boubrahimi. “It also creates a radiation exposure hazard for aircraft travelers, as well as astronauts aboard the International Space Station.”
In 1989, she notes, a geomagnetic storm from the sun caused a nine-hour outage of Canada’s Hydro-Québec electricity transmission system.
“We depend on satellites for so many of our daily activities, including using mobile phones, surfing the Web and TV viewing, as well as our energy, defense and space systems,” says Filali Boubrahimi, assistant professor in USU’s Department of Computer Science. “We need the ability to better predict solar activity and prepare for these potentially harmful conditions.”
To that end, Filali Boubrahimi is developing novel machine learning models to better predict solar flares and thereby enable Earth’s inhabitants and space travelers to better prepare for the sun’s stormy behavior. She was recently awarded a National Science Foundation Faculty Early Career Development “CAREER” award to pursue this challenge. A sole principal investigator, Filali Boubrahimi is the recipient of a five-year, $691,972 CAREER grant to support her project, End-to-End Active Region-based Heliospheric Forecasting System using Multi-spacecraft Data and Machine Learning.
While NSF-funded solar observatories, including Big Bear Solar Observatory, and scientific agencies such as NASA and NOAA have honed observations of the sun’s activity cycles, significant barriers to accurate prediction of solar flares remain. To address these challenges, Filali Boubrahimi and her students will develop a high-spatial resolution active regions vector magnetogram dataset, spanning two solar cycles and based on real data collected from NASA’s space-borne Solar Dynamics Observatory (SDO) and Solar and Heliospheric Observatory (SOHO), as well as Japan’s Hinode satellite.
“Our goal is to create a comprehensive solar flare catalog combing images and data, and to leverage it to develop predictive, data-driven models ready to be deployed in an operational environment,” she says. “We will share our findings with the scientific community in important annual meetings, including the NSF’s Solar, Heliospheric, and INterplanetary Environment (SHINE) workshop.”
Filali Boubrahimi’s CAREER grant bolsters funding from a three-year NSF grant she received in 2022. In addition to providing research opportunities for graduate and undergraduate students in her lab, she plans to continue offering learning activities for participants in USU’s Native American Summer Mentorship Program.
She says mining the solar activity datasets is timely, as NASA and NOAA’s Solar Cycle Prediction Panel declared the beginning of Solar Cycle 25 in December 2019, starting with a solar minimum — or time of lessened solar activity.
“The panel foresees this current cycle will ramp up toward a solar maximum in July 2025,” Filali Boubrahimi says. “By that time, we hope to have our models ready for high solar activity.”
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