Researchers developed a neural network deep learning technique to extract hidden turbulent motion information from observations of the sun. Scientists tested on three different sets of simulation. Data showed that it is possible to infer the horizontal motion from data for the temperature and vertical motion. This process will benefit solar astronomy and other fields such as plasma physics, fusion science and fluid dynamics.
The sun is important to the Sustainable Development Goal of Affordable and Clean Energy. This is as the source of solar power and as a natural example of fusion energy. Our understanding of the sun is limited by the data we can collect. It is relatively easy to observe the temperature and vertical motion of solar plasma and gas. It is so hot that the component atoms break down into electrons and ions. Though it is difficult to determine the horizontal motion.
Scientists of National Astronomical Observatory of Japan and National Institute for Fusion Science created a neural network model. They fed it data from three different simulations of plasma turbulence. The neural network was able to correctly infer the horizontal motion given only the vertical motion and the temperature.
Scientists developed a novel coherence spectrum to evaluate the performance of the output at different size scales. The new analysis showed that the method succeeded at predicting the large-scale patterns in the horizontal turbulent motion. But it had trouble with small features. Scientists are now working to improve the performance at small scales. Scientists are hoping that this method can be applied to future high resolution solar observations like those expected from the SUNRISE-3 balloon telescope.
The study has been published in Astronomy and Astrophysics.