Auto−calibration and reconstruction of SDO’s Atmospheric Imaging Assembly channels with Deep Learning

Abstract

Solar activity has a major role in influencing space weather and the interplanetary medium. Understanding the complex mechanisms that govern such a dynamic phenomenon is important and challenging. Remote-sensing instruments on board of heliophysics missions can provide a wealth of information on the Sun's activity, especially via the measurement of magnetic fields and the emission of light from the multi-layered Sun's atmosphere. Ever since its launch in 2010, the observations by NASA’s Solar Dynamics Observatory (SDO) generates terabytes of observational data every day and has constantly monitored the Sun 24x7 with the highest time cadence and spatial resolution for full-disk observations. Using the enormous amount of data SDO provides, this project, developed at the NASA’s Frontier Development Lab (FDL 2019), focuses on algorithms that enhance our understanding of the Sun, as well as enhance the observation potential of present and future heliophysics missions with the aid of machine learning. In the present work, we use deep learning to increase the capabilities of NASA’s SDO and focus primarily on two aspects: (1) develop a neural network that auto-calibrates the SDO-AIA channels, which suffer from steady degradation over time; and (2) develop a “virtual telescope” that enlarges the missions possibilities by synthetically generating desired EUV channels derived from actual physical equipment flown on other mission. Towards this end, we use a deep neural network structured as an encoder-decoder to artificially generate images in different wavelengths from a limited number of observations. This approach can also improve other existing as well as the concept development of future missions that do not have as many observing instruments as SDO.