Machine learning for reconstruction of polarity inversion lines from solar filaments

TL;DR

This paper introduces a machine learning approach to reconstruct solar magnetic polarity maps from filament observations, enabling automated, uncertain-aware, and historically valuable magnetic field reconstructions.

Contribution

The authors develop a novel machine learning method that automates the reconstruction of polarity maps from filament data, incorporating prior information and uncertainty estimation.

Findings

The model produces polarity maps close to manual ones.

Incorporating prior points improves reconstruction accuracy.

Uncertainty estimates help assess the reliability of the maps.

Abstract

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.