用于从太阳暗条重建极性反转线的机器学习
Machine learning for reconstruction of polarity inversion lines from solar filaments
AI总结：
提出一种用户引导的机器学习方法，利用历史太阳暗条观测和参考点极性信息重建磁极性图，结果接近手绘极性图并支持不确定性估计。
摘要：
太阳暗条是众所周知的极性反转线示踪物，这些反转线分隔太阳光球层上两个相反的磁极性。由于对暗条的观测远早于对太阳磁场的系统观测，历史暗条目录可有助于重建尚无直接磁场观测时期的磁极性图。在实践中，这种重建通常具有歧义，而且一般由人工完成。我们提出一种基于机器学习模型的自动方法，该模型可生成与暗条观测相一致的多种磁极性图。为评估模型并讨论结果，我们使用由 McIntosh 汇编的太阳暗条目录和极性图。我们认识到，人工汇编极性图的过程不仅包含暗条信息，还包含大量难以形式化的先验信息。为弥补机器学习模型先验知识的不足，我们向其提供若干参考点处的极性信息。我们证明，这一可被视为用户引导重建或超分辨率的过程，可生成与手绘图相当接近的极性图，并且还能够进行不确定性估计。
英文摘要：
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.
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