Generation of Solar EUV images from HMI magnetograms by deep learning

In astronomy and geophysics, multi-wavelength observations become very popular. Recently, several deep learning methods for image-to-image translations have been suggested and are successful for different types of transformation such as labels to street scene, black and white images to color ones, aerial to map, day to night, and sketch images to pictures. In this study, we apply an image-to-image translation model, based on conditional Generative Adversarial Networks (cGANs), to construct solar EUV images using solar magnetograms. For this, we train the model using pairs of SDO/AIA EUV image and their corresponding SDO/HMI line-of-sight magnetogram for all AIA wavelengths from 2011 to 2017 except September and October. We test the model by comparing pairs of actual SDO/AIA EUV images and corresponding AI-generated ones in September and October. We find that both real and AI-generated images are quite consistent with each other in that it is difficult for one to distinguish solar EUV images from AI-generated ones. Especially, 193 and 211 data sets have the best average correlation values (0.907 and 906) between actual EUV images and AI-generated ones for test data sets, being consistent with the idea that the origin of coronal heating is magnetic field. Using this model, we construct solar EUV images with Kitt peak magnetograms since 1974. This methodology can be applicable to many scientific fields that use several different filter images.