New Delhi: Scientists have made a breakthrough in solar physics by using artificial intelligence (AI) to simulate the magnetic field in the Sun’s upper atmosphere in near real time. The research, published in Nature Astronomy, holds immense potential for advancing our understanding of the Sun’s behavior and its impact on space weather.
The solar magnetic field is the main driver of space weather, which can damage critical infrastructure such as power, aviation and our space-based technology. The main source of severe space weather events are solar active regions, which are regions around sunspots where strong magnetic fields emerge through the solar surface.
Current observational capabilities only allow measuring the magnetic field at the Sun’s surface, however, as energy is created and released high up in the solar atmosphere, the Sun’s corona.
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By taking advantage of the capabilities of physics-informed neural networks, the team from the University of Graz in Austria and the Skolkovo Institute of Science and Technology in Russia successfully managed to integrate observational data with physical force-free magnetic field models.
They provided a comprehensive understanding of the relationship between observed phenomena and the underlying physics governing the Sun’s activity.
This state-of-the-art method is an important milestone in solar physics and opens new opportunities for numerical simulations of the Sun. The researchers simulated the evolution of an observed solar active region and demonstrated the ability to perform force-free magnetic field simulations in real time.
Impressively, this process requires just under 12 hours of computation time to simulate an observation series of five days. This unprecedented speed enables scientists to analyze and forecast solar activity in real time, increasing our ability to predict space weather events.
“Our use of artificial intelligence represents a transformative leap forward in this context,” said Robert Jarolim, lead researcher at the university. The use of AI techniques for numerical simulations allows us to better incorporate observational data and improve our holds great potential to further enhance the simulation capabilities.” of Graz.
“The computing speed holds significant promise for improving space weather forecasting and advancing our knowledge of the Sun’s behavior,” said Tatiana Podlachikova, Associate Professor at Skoltech.
The team studied the time evolution of free magnetic energy within the coronal volume, which is associated with solar eruption events on the Sun such as coronal mass ejections – large plasma clouds ejected from the Sun’s atmosphere at speeds of 100–3,500 km/s. Let’s get out S.
Comparison to extreme ultraviolet observations confirmed the robustness and accuracy of the methodology. Importantly, the results revealed a significant reduction of free magnetic energy, both spatially and temporally, which is directly related to the observed solar outbursts.