Obtaining space-based SDO/AIA solar UV and EUV images from ground-based Hα observations by deep learning

Abstract

In this work, we explore the mappings from solar images taken in Hα (6563 Å) by the Global Oscillation Network Group (GONG) on the ground to those observed in eight different wavelengths (94, 131, 171, 193, 211, 304, 335 and 1600 Å) by SDO/AIA in space. Eight mappings are built by training the conditional Generative Adversarial Networks (cGANs) on datasets with 500 paired images, which are [Hα, AIA94], [Hα, AIA131], [Hα, AIA171], [Hα, AIA193], [Hα, AIA211], [Hα, AIA304], [Hα, AIA335] and [Hα, AIA1600]. We evaluate the eight trained cGANs models on validation and test datasets with 154-pair images and 327-pair images, respectively. The model generated fake AIA images match the corresponding observed AIA images well on large-scale structures such as large active regions and prominences. But the small-scale flare loops and filament threads are difficult to reconstruct. Four quantitative comparisons are carried out on the validation and test datasets to score the mappings. We find that the model-generated images in 304 and 1600 Å match the corresponding observed images best. This exploration suggests that the cGANs are promising methods for mappings between ground-based Hα and space-based EUV/UV images, while some improvements are necessary.