Machine learning for reconstruction of polarity inversion lines from solar filaments
This work addresses the need for automated reconstruction of solar magnetic polarity maps for astronomers, offering a more efficient alternative to manual methods, though it is incremental as it builds on existing catalogs and prior knowledge.
The authors tackled the problem of automatically reconstructing solar magnetic polarity maps from historical filament observations, which are often ambiguous and manually compiled, by developing a machine-learning model that generates multiple consistent maps with user-guided reference points, achieving results reasonably close to hand-drawn maps and enabling uncertainty estimation.
Solar filaments are well-known tracers of polarity inversion lines that separate two opposite magnetic polarities on the solar photosphere. Because observations of filaments began long before the systematic observations of solar magnetic fields, historical filament catalogs can facilitate the reconstruction of magnetic polarity maps at times when direct magnetic observations were not yet available. In practice, this reconstruction is often ambiguous and typically performed manually. We propose an automatic approach based on a machine-learning model that generates a variety of magnetic polarity maps consistent with filament observations. To evaluate the model and discuss the results we use the catalog of solar filaments and polarity maps compiled by McIntosh. We realize that the process of manual compilation of polarity maps includes not only information on filaments, but also a large amount of prior information, which is difficult to formalize. In order to compensate for the lack of prior knowledge for the machine-learning model, we provide it with polarity information at several reference points. We demonstrate that this process, which can be considered as the user-guided reconstruction or super-resolution, leads to polarity maps that are reasonably close to hand-drawn ones, and additionally allows for uncertainty estimation.