Energetic flares on the sun can cause disturbances in the Earth’s magnetic field, which can affect technical systems in space and on Earth. Researchers from the University of Graz and the Skoltech Institute in Moscow have developed a method that should make such massive events more predictable using AI. It was in the latest issue of the journal Nature Astronomy.
The sun is constantly emitting radiation and charged particles into space. If this particle stream (solar wind) is significantly stronger in a limited area of the sun for a short time, it is called a solar flare. The Earth is actually protected by its magnetic field and atmosphere, explains Robert Jarolim of the Institute of Physics at the University of Graz. However, when the high-energy radiation and particles released by a solar flare hit the Earth’s magnetic field during massive solar storms, it can lead to significant damage: power grids or communication and navigation systems and air traffic can collapse, satellites destroyed.
Better understanding of space weather
Efforts have been made for decades to make more accurate predictions of such events and possible effects through a thorough understanding of the high-energy processes. “We need to better understand the relationship between our observations and the underlying physics, better describe the temporal development and model it to better understand the space weather from simulations,” says Jarolim.
To better understand and monitor these phenomena, the Sun is constantly monitored by a network of ground-based observatories and space telescopes. Together with colleagues from Moscow, the young physicist from Graz has succeeded in simulating the magnetic field in the upper layers of the solar atmosphere, where eruptions take place. “Measuring is not possible in these areas. We therefore need modeling to describe and understand the processes,” explains the lead author of the “Nature” publication.
The research team focuses on the sunspots. “These are areas with a very strong magnetic field,” says Jarolim. He wants to use artificial intelligence to advance automated observation. The physicist, who has years of experience in software development, uses neural networks for this. “We can integrate data more flexibly and thus find more flexible solutions,” says Jarolim.
The data is combined with physical models to simulate what happens in the upper layers of the solar atmosphere. With the new method, the duration of the calculation can also be “significantly shortened”. This makes it possible to “update new data in near real time,” says the astrophysicist. The simulation of five observation days therefore required a total of less than twelve hours of calculation work.
Source: Krone
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