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Russian neural network will help predict space weather
Moscow State University named after M.IN. Lomonosov Moscow State University (MSU) reports on the development of a neural network capable of identifying coronal holes – active regions on the Sun that lead to geomagnetic storms.
Usually, when making accurate forecasts of geomagnetic storms, manual processing of the images of our star is performed. This is a rather time-consuming process and can be time-consuming. New machine learning technology reduces analysis time to fractions of a second.
Specialists from the Probability Theory Department of the Faculty of Mechanics and Mathematics of Moscow State University, as well as their colleagues from the Kislovodsk Mountain Astronomical Station and the New Jersey Institute of Technology, took part in the work.
The authors of the study proposed a new method for detecting coronal holes – a model that was trained on a large sample of solar images. The solution is based on an ultra-precise neural network.
“The developed machine learning model can detect active regions (coronal holes) at a level comparable to expert judgment. At the same time, unlike an expert, the model does not get tired, does not lose attention and spends a fraction of a second on one shot: the speed of the model makes it possible to analyze the images online, “the MSU publication says.
The developed technique is supposed to help predict space weather. And this will make it possible to prevent risks associated with the stability of the operation of satellite and telecommunication systems.