Generation of Modern Satellite Data from Galileo Sunspot Drawings in 1612 by Deep Learning

Abstract

Abstract Historical sunspot drawings are very important resources for understanding past solar activity. We generate solar magnetograms and EUV images from Galileo sunspot drawings using a deep learning model based on conditional generative adversarial networks. We train the model using pairs of sunspot drawings from the Mount Wilson Observatory and their corresponding magnetograms (or UV/EUV images) from 2011 to 2015 except for every June and December by the Solar Dynamic Observatory satellite. We evaluate the model by comparing pairs of actual magnetograms (or UV/EUV images) and the corresponding AI-generated ones in June and December. 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 consistent with those of the original ones. Applying this model to the Galileo sunspot drawings in 1612, we generate Helioseismic and Magnetic Imager-like magnetograms and Atmospheric Imaging Assembly-like EUV images of the sunspots. We hope that the EUV intensities can be used for estimating solar EUV irradiance at long-term historical times.