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Here we greatly improve Artificial Intelligence (AI)-generated solar farsidemagnetograms using data sets of Solar Terrestrial Relations Observatory(STEREO) and Solar Dynamics Observatory (SDO). We modify our previous deeplearning model and configuration of input data sets to generate more realisticmagnetograms than before. First, our model, which is called Pix2PixCC, usesupdated objective functions which include correlation coefficients (CCs)between the real and generated data. Second, we construct input data sets ofour model: solar farside STEREO extreme ultraviolet (EUV) observations togetherwith nearest frontside SDO data pairs of EUV observations and magnetograms. Weexpect that the frontside data pairs provide the historic information ofmagnetic field polarity distributions. We demonstrate that magnetic fielddistributions generated by our model are more consistent with the real onesthan before in view of several metrics. The averaged pixel-to-pixel CC for fulldisk, active regions, and quiet regions between real and AI-generatedmagnetograms with 8 by 8 binning are 0.88, 0.91, and 0.70, respectively. Totalunsigned magnetic flux and net magnetic flux of the AI-generated magnetogramsare consistent with those of real ones for test data sets. It is interesting tonote that our farside magnetograms produce consistent polar field strengths andmagnetic field polarities with those of nearby frontside ones for solar cycle24 and 25. Now we can monitor the temporal evolution of active regions usingsolar farside magnetograms by the model together with the frontside ones. OurAI-generated Solar Farside Magnetograms (AISFMs) are now publicly available atKorean Data Center (KDC) for SDO.

Improved AI-generated Solar Farside Magnetograms by STEREO and SDO Data Sets and Their Release