Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data with Deep Learning
Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data with Deep Learning
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DOI：
10.1007/s11207-023-02180-z
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发表时间：
2022-11
期刊：
影响因子：
2.8
通讯作者：
Haodi Jiang;Qin Li;Nian Liu;Zhihang Hu;Yasser Abduallah;J. Jing;Yan Xu;J. T. Wang;Haimin Wang
中科院分区：
文献类型：
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作者：
Haodi Jiang;Qin Li;Nian Liu;Zhihang Hu;Yasser Abduallah;J. Jing;Yan Xu;J. T. Wang;Haimin Wang
太阳活动通常是由太阳磁场的演变引起的。由太阳活动区的光球矢量磁图导出的磁场参数已被用于分析和预报太阳耀斑和日冕物质抛射等喷发事件。不幸的是，最近的太阳周期24相对较弱，很少有大耀斑，尽管这是自2010年发射以来唯一一个通过太阳动力学观测站（SDO）上的日震和磁成像仪（HMI）获得一致的时间序列矢量磁图的太阳周期。在本文中，我们研究了另一个主要的仪器，即1996年至2010年太阳和日光层天文台（SOHO）上的迈克尔逊多普勒成像仪（MDI）。SOHO/MDI的数据档案涵盖了更活跃的太阳周期23，有许多大的耀斑。然而，SOHO/MDI数据只有视距（LOS）磁图。本文提出了一种新的深度学习方法——MagNet，利用SDO/HMI拍摄的联合LOS磁图Bx和By，以及大熊太阳天文台（BBSO）收集的H-alpha观测数据，生成矢量分量Bx‘和By’，将观测到的LOS数据组成矢量磁图。通过这种方式，我们可以将矢量磁图的可用性扩展到1996年至今。实验结果证明了该方法的良好性能。据我们所知，这是第一次使用深度学习来使用SDO/HMI和H-alpha数据为SOHO/MDI生成太阳活动区的光球矢量磁图。
Solar activity is usually caused by the evolution of solar magnetic fields. Magnetic field parameters derived from photospheric vector magnetograms of solar active regions have been used to analyze and forecast eruptive events such as solar flares and coronal mass ejections. Unfortunately, the most recent solar cycle 24 was relatively weak with few large flares, though it is the only solar cycle in which consistent time-sequence vector magnetograms have been available through the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) since its launch in 2010. In this paper, we look into another major instrument, namely the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO) from 1996 to 2010. The data archive of SOHO/MDI covers more active solar cycle 23 with many large flares. However, SOHO/MDI data only has line-of-sight (LOS) magnetograms. We propose a new deep learning method, named MagNet, to learn from combined LOS magnetograms, Bx and By taken by SDO/HMI along with H-alpha observations collected by the Big Bear Solar Observatory (BBSO), and to generate vector components Bx' and By', which would form vector magnetograms with observed LOS data. In this way, we can expand the availability of vector magnetograms to the period from 1996 to present. Experimental results demonstrate the good performance of the proposed method. To our knowledge, this is the first time that deep learning has been used to generate photospheric vector magnetograms of solar active regions for SOHO/MDI using SDO/HMI and H-alpha data.