Generation of Solar Magnetograms, UV, and EUV images from Galileo Sunspot Drawings by Deep Learning

Abstract:

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 the model using pairs of sunspot drawing from Mount Wilson Observatory (MWO) and their corresponding SDO/HMI magnetogram (or SDO/AIA images) from 2011 to 2015 except for every June and December. We evaluate the model by comparing pairs of actual magnetogram (UV/EUV image) and the corresponding AI-generated one in June and December. Our results show that bipolar structures of the AI-generated magnetograms are similar to those of the original ones and their unsigned magnetic fluxes (or intensities) are consistent with those of the original ones. Applying this model to the Galileo sunspot drawings in 1612, we generate HMI-like magnetograms and AIA-like EUV images of the sunspots. We hope that the EUV intensities can be used for estimating solar EUV irradiance at historical times.

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).