EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Multi-Channel Coronal Hole Detection with Convolutional Neural Networks

Robert Jarolim1, Astrid Veronig1,2, Stefan Hofmeister3, Stephan Heinemann1, Manuela Temmer1, Tatiana Podladchikova4, and Karin Dissauer1,5
Robert Jarolim et al.
  • 1University of Graz, Institute of Physics, Universitätsplatz 5, 8010 Graz, Austria
  • 2University of Graz, Kanzelhöhe Observatory for Solar and Environmental Research, Kanzelhöhe 19, 9521 Treffen am Ossiacher See, Austria
  • 3Columbia Astrophysics Laboratory, Columbia University, 550 West 120th Street, New York, NY 10027, USA
  • 4Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1, Moscow 121205, Russia
  • 5NorthWest Research Associates, 3380 Mitchell Ln, Boulder, CO 80301, USA

Being the source region of fast solar wind streams, coronal holes are one of the key components which impact space weather. The precise detection of the coronal hole boundary is an important criterion for forecasting and solar wind modeling, but also challenges our current understanding of the magnetic structure of the Sun. We use deep-learning to provide new methods for the detection of coronal holes, based on the multi-band EUV filtergrams and LOS magnetogram from the AIA and HMI instruments onboard the Solar Dynamics Observatory. The proposed neural network is capable to simultaneously identify full-disk correlations as well as small-scale structures and efficiently combines the multi-channel information into a single detection. From the comparison with an independent manually curated test set, the model provides a more stable extraction of coronal holes than the samples considered for training. Our method operates in real-time and provides reliable coronal hole extractions throughout the solar cycle, without any additional adjustments. We further investigate the importance of the individual channels and show that our neural network can identify coronal holes solely from magnetic field data.

How to cite: Jarolim, R., Veronig, A., Hofmeister, S., Heinemann, S., Temmer, M., Podladchikova, T., and Dissauer, K.: Multi-Channel Coronal Hole Detection with Convolutional Neural Networks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1490,, 2021.

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