MAGiDiff：从UV/EUV滤光图采样光球矢量场

中文摘要 AI 辅助

光球矢量磁场是建模、理解和预测太阳活动的基础。这些数据通常通过对多个波段的全斯托克斯矢量进行反演和消歧来生成，这一过程要求很高。在此，我们研究了从UV/EUV滤光图估计光球矢量磁图的能力。由于缺乏偏振信息，该问题具有挑战性且本质上具有模糊性，因为从UV/EUV强度到磁场的映射是间接且不适定的。我们引入了MAGiDiff，一种基于机器学习的方法，使用去噪扩散模型从UV/EUV滤光图估计矢量磁图。作为输入，MAGiDiff接收来自太阳动力学观测站（SDO）/大气成像组件（AIA）的一组滤光图；作为输出，它被训练来估计由日出卫星（Hinode）/太阳光学望远镜-光谱偏振仪（SOT-SP）观测到的消歧后的矢量磁图。我们表明，MAGiDiff能够准确模仿Hinode的真实数据。此外，我们探究了MAGiDiff对物理结构和磁连接的理解决。在全日面上，我们表明它为活动区产生了合理的结构。尽管存在半球极性反转，MAGiDiff仍能跨太阳周期泛化，并可微调至其他EUV仪器，包括STEREO/EUVI和GOES-R/SUVI。虽然显然不能替代专用仪器，但MAGiDiff开启了新的能力之门。

英文摘要

Photospheric vector magnetic fields are foundational to modeling, understanding, and forecasting solar activity. These data are usually produced by inverting and disambiguating the full Stokes vector at multiple passbands, which is demanding. Here, we investigate how well we can estimate photospheric vector magnetograms from UV/EUV filtergrams. This problem is challenging and intrinsically ambiguous without polarization information, as the mapping from UV/EUV intensity to the magnetic field is indirect and ill-posed. We introduce MAGiDiff, a machine-learning-based method that uses denoising diffusion models to estimate vector magnetograms from UV/EUV filtergrams. As input, MAGiDiff takes a stack of filtergrams from the Solar Dynamics Observatory (SDO) / Atmospheric Imaging Assembly (AIA); as output, it is trained to estimate the disambiguated vector magnetogram as seen by Hinode / Solar Optical Telescope-Spectro-Polarimeter (SOT-SP). We show that MAGiDiff can accurately mimic the Hinode ground-truth. Additionally, we probe MAGiDiff's understanding of the physical structure and magnetic connectivity. On full-disk, we show that it produces plausible structures for active regions. MAGiDiff generalizes across solar cycles despite hemispheric polarity reversal, and can be fine-tuned to other EUV instruments including STEREO/EUVI and GOES-R/SUVI. While clearly not a substitute for a dedicated instrument, MAGiDiff opens the door to new capabilities.

发表机构

• New York University（纽约大学）

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