RHESSI17--GAN

Atmospheric seeing is ubiquitous for ground-based astronomy. Atmospheric seeing produces distortions in images due to varying density and temperature structure of the Earth’s atmosphere. Bad seeing can be accounted for in part by adaptive optics built into ground-based instruments. However, with the newer generation of higher resolution ground-based instruments AO systems cannot act quickly enough to remove the worst seeing from images. As a result, we propose a generative adversarial network (GAN) which will learn how to remove blur and distortions simulated onto space-based data from SOT. The goal of this network is to generate solar flare images indistinguishable from ground-truth solar flare images. This is done through a kernel-free approach to deblurring and is a single-frame blind deconvolution method. The single-frame is important due to the low cadence of observations with respect to flare timescales meaning that a multi-frame approach could result in lost information. With the ability to generate these images, the model can then be applied to data with real seeing and they can be reconstructed with high accuracy to be included in our datasets for data analysis. The results are that spectroscopic and spectropolarimetric line profiles are successfully reconstructed by our network and so are feasible to be used for further data analysis.