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AI Spots Coronal Holes to Automate Space Weather Prediction

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Observation of the solar dynamic observatory (SDO). The image shows a composite of the seven different extreme-ultraviolet filters (colored slices) and the magnetic field information (gray scale slice).

Researchers at Austria's University of Graz developed a convolutional neural network called CHRONNOS (Coronal Hole RecOgnition Neural Network Over multi-Spectral-data) to detect holes in the sun's corona.

Credit: Jarolim et. al., 2021

An international team of scientists brought automated space weather prediction a step closer to reality via a neural network that can identify coronal holes—gaps in the solar atmosphere left by particles that cause geomagnetic storms on Earth—in space-based observations.

Robert Jarolim at Austria's University of Graz said CHRONNOS (Coronal Hole RecOgnition Neural Network Over multi-Spectral-data) applies artificial intelligence to spot coronal holes "based on their intensity, shape, and magnetic field properties, which are the same criteria as a human observer takes into account."

The team trained the convolutional neural network on about 1,700 extreme ultraviolet wavelength images of the sun's corona recorded in 2010-2017, and compared its results to 261 manually identified coronal holes.

CHRONNOS matched human performance in 98% of the cases, and outperformed humans in identifying coronal holes from magnetic field maps.

From Skolkovo Institute of Science and Technology (Russia)
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Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA


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