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.

翻译：太阳暗条是区分太阳光球层上两个相反磁极性的极性反转线的著名示踪物。由于对暗条的观测远早于太阳磁场的系统观测，历史暗条目录能够重建尚无直接磁场观测时期的磁极性图。在实际操作中，这种重建往往具有模糊性，且通常由人工完成。我们提出了一种基于机器学习模型的自动方法，该方法可生成与暗条观测一致的多种磁极性图。为了评估模型并讨论结果，我们使用了McIntosh编制的太阳暗条目录和极性图。我们认识到，人工编制极性图的过程不仅包含暗条信息，还涉及大量难以形式化的先验信息。为了弥补机器学习模型缺乏先验知识的不足，我们为其提供了若干参考点处的极性信息。我们证明，这一可视为用户引导重建或超分辨率的过程，能够产生与手绘极性图相当接近的结果，并且还能实现不确定性估计。