2018/09/15 by Egor Illarionov, E. Illarionov, A. Tlatov · 52 citations
Computer Science · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Computer science #Convolutional neural network #Image (mathematics) #Image segmentation #Pattern recognition (psychology) #Physics #Segmentation #Solar Radiation and Photovoltaics #Solar and Space Plasma Dynamics #Solar cycle #Solar wind #Stellar, planetary, and galactic studies #Thresholding #astro-ph.SR
paper · pdf · doi:10.1093/mnras/sty2628
published in Monthly Notices of the Royal Astronomical Society 481(4), 5014-5021 (Oxford University Press)
arxiv created 2018/09/15 · openalex created_date 2018/09/27 · openalex publication_date 2018/09/27 · arxiv updated 2018/10/17 · openalex updated_date 2026/08/05
Current coronal holes (CHs) segmentation methods typically rely on image thresholding and require non-trivial image pre- and post-processing. We have trained a neural network that accurately isolates CHs from SDO/AIA 193 Angstrom solar disc images without additional complicated steps. We compare results with publicly available catalogues of CHs and demonstrate stability of the neural network approach. In our opinion, this approach can outperform hand-engineered solar image analysis and will have a wide application to solar data. In particular, we investigate long-term variations of CH indices within the solar cycle 24 and observe increasing of CH areas in about three times from minimal values in the maximum of the solar cycle to maximal values during the declining phase of the solar cycle.