A Machine Learning Approach to Correcting Atmospheric Seeing in Solar Flare Observations

Abstract: Current post-processing techniques for the correction of atmospheric seeing in solar observations -- such as Speckle interferometry and Phase Diversity methods -- have limitations when it comes to their reconstructive capabilities of solar flare observations. This, combined with the sporadic nature of flares meaning observers cannot wait until seeing conditions are optimal before taking measurements, means that many ground-based solar flare observations are marred with bad seeing. To combat this, we propose a method for dedicated flare seeing correction based on training a deep neural network to learn to correct artificial seeing from flare observations taken during good seeing conditions. This model uses transfer learning, a novel technique in solar physics, to help learn these corrections. Transfer learning is when another network already trained on similar data is used to influence the learning of the new network. Once trained, the model has been applied to two flare datasets: one from AR12157 on 2014/09/06 and one from AR12673 on 2017/09/06. The results show good corrections to images with bad seeing with a relative error assigned to the estimate based on the performance of the model. Further discussion takes place of improvements to the robustness of the error on these estimates.