Deep neural network to find hidden turbulent motion on the sun

Date:
February 25, 2022
Source:
National Institutes of Natural Sciences
Summary:
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.



FULL STORY ========================================================================== 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.

========================================================================== Story Source: Materials provided by
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========================================================================== Journal Reference:
1. Ryohtaroh T. Ishikawa, Motoki Nakata, Yukio Katsukawa, Youhei
Masada,
Tino L. Riethmu"ller. Multi-scale deep learning for estimating
horizontal velocity fields on the solar surface. Astronomy &
Astrophysics, 2022; 658: A142 DOI: 10.1051/0004-6361/202141743 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/02/220225100237.htm

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