Multi−Channel Auto−Calibration for the Atmospheric Imaging Assembly instrument with Deep Learning

Abstract

Solar activity plays a major role in influencing the interplanetary medium and space-weather around us. Understanding the complex mechanisms that govern such a dynamic phenomenon is important and challenging. Remote-sensing instruments onboard 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 solar atmosphere. NASA currently operates the Heliophysics System Observatory (HSO) that consists of a fleet of satellites constantly monitoring the Sun, its extended atmosphere, and space environments around the Earth and other planets of the solar system. One of the flagship missions of the HSO is NASA’s Solar Dynamics Observatory (SDO). Launched in 2010, it consists of three instruments: the Atmospheric Imaging Assembly (AIA), the Helioseismic & Magnetic Imager (HMI), and the EUV Variability Experiment (EVE). The SDO has been generating terabytes of observational data every day and has constantly monitored theSun with the highest temporal and spatial resolution for full-disk observations. Unfortunately, the (E)UV instruments in orbit suffer time-dependent degradation, which reduces instrument sensitivity. Accurate calibration for EUV instruments currently depends on sounding rockets (e.g., for SDO/EVE and SDO/AIA) infrequent. Since SDO is in a geosynchronous orbit, sounding rockets can be used for calibration, but calibration experiments may not be practical for deep space missions (e.g., STEREO satellites). In the present work, we develop a neural network that auto-calibrates the SDO/AIA channels, correcting sensitivity degradation, by exploiting spatial patterns in multi-wavelength observations to arrive at a self-calibration (E)UV imaging instruments. This removes a major impediment to developing future HSO missions that can deliver solar observations from different vantagepoints beyond Earth-orbit.