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Solar activity is usually caused by the evolution of solar magnetic fields.Magnetic field parameters derived from photospheric vector magnetograms ofsolar active regions have been used to analyze and forecast eruptive eventssuch as solar flares and coronal mass ejections. Unfortunately, the most recentsolar cycle 24 was relatively weak with few large flares, though it is the onlysolar cycle in which consistent time-sequence vector magnetograms have beenavailable through the Helioseismic and Magnetic Imager (HMI) on board the SolarDynamics Observatory (SDO) since its launch in 2010. In this paper, we lookinto another major instrument, namely the Michelson Doppler Imager (MDI) onboard the Solar and Heliospheric Observatory (SOHO) from 1996 to 2010. The dataarchive 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 anew deep learning method, named MagNet, to learn from combined LOSmagnetograms, Bx and By taken by SDO/HMI along with H-alpha observationscollected by the Big Bear Solar Observatory (BBSO), and to generate vectorcomponents Bx' and By', which would form vector magnetograms with observed LOSdata. In this way, we can expand the availability of vector magnetograms to theperiod from 1996 to present. Experimental results demonstrate the goodperformance of the proposed method. To our knowledge, this is the first timethat deep learning has been used to generate photospheric vector magnetogramsof solar active regions for SOHO/MDI using SDO/HMI and H-alpha data.

A Deep Learning Approach to Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data