Conceptual diagram of this work. Given the observable vertical motion and surface temperature, the neural network is able to infer the horizontal motion. Credit: NAOJ Original size (1.3MB)
Scientists developed a neural network deep learning technique to
extract hidden turbulent motion information from observations of the
Sun. Tests 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 technique 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, both 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, gas so hot that the
component atoms break down into electrons and ions. But it is difficult
to determine the horizontal motion.
To tackle this problem, a team of scientists led by the National
Astronomical Observatory of Japan and the National Institute for Fusion
Science created a neural network model, and fed it data from three
different simulations of plasma turbulence. After training, the neural
network was able to correctly infer the horizontal motion given only the
vertical motion and the temperature.
The team also developed a novel coherence spectrum to evaluate the
performance of the output at different size scales. This new analysis
showed that the method succeeded at predicting the large-scale patterns
in the horizontal turbulent motion, but had trouble with small features.
The team is now working to improve the performance at small scales. It
is hoped that this method can be applied to future high resolution solar
observations, such as those expected from the SUNRISE-3 balloon
telescope, as well as to laboratory plasmas, such as those created in
fusion science research for new energy.
These results appeared as Ishikawa et al. “Multi-Scale Deep Learning for Estimating Horizontal Velocity Fields on the Solar Surface” in the online edition of Astronomy and Astrophysics on February 16, 2022.
Related Links
Related Links
- Deep Neural Network to Find Hidden Turbulent Motion on the Sun -New Development in Research for Solar and Plasma Turbulence- (Solar Science Observatory)
- Deep Neural Network to Find Hidden Turbulent Motion on the Sun - New Development in Research for Solar and Plasma Turbulence - (NIFS)
- Deep Neural Network to Find Hidden Turbulent Motion on the Sun (SOKENDAI)
