Application of Deep Learning to Solar and Space Weather Data: 5. Generation of Historic Solar Magnetograms and UV images

We apply an image-to-image translation model, which is a popular deep learning method based on conditional Generative Adversarial Networks (cGANs), to the generation from sunspot drawings to the corresponding magnetograms and EUV images. For this, we train a model using pairs of sunspot drawing from Mount Wilson Observatory (MWO) and their corresponding SDO/HMI magnetogram (or SDO/AIA images) from 2012 to 2013. We test the model by comparing pairs of actual magnetogram (EUV image) and the corresponding AI-generated one in 2014. Our results show that bipolar structures of the AI-generated magnetograms are consistent with those of the original ones and their unsigned magnetic fluxes (or intensities) are well consistent with those of the original ones. Using this model with the Carrington sunspot drawing, we successfully produce AI-generated magnetogram (EUV image) and estimate its unsigned magnetic flux. Using several empirical relationships (magnetic flux vs. CME speed, CME speed vs. ICME speed, and ICME speed vs. Dst) in 23 and 24th solar cycle, we conjecture the Dst value of the Carrington event, about -1,670nT, which is similar to that of Tsurutani et al. (2003). Applying this model to the Galileo sunspot drawings in 1612, we generate HMI-like magnetograms and AIA-like EUV images of the sunspots.

NOTE: This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (2018-0-01422, Study on analysis and prediction technique of solar flares).