AI to Enhance the Capabilities of EUV-observing Satellites and Estimate Spectral Irradiance

Multiple satellites capture images of the Sun in Extreme Ultraviolet (EUV) light. However, only the Solar Dynamics Observatory (SDO) was equipped with instruments that measure the Sun's EUV spectral irradiance (i.e., MEGS-A and MEGS-B onboard the Extreme Ultraviolet Variability Experiment (EVE) suite). The MEGS-A instrument malfunctioned in 2014, making it impossible to measure the full irradiance spectrum ever since. 

 

Using AI, we explore the translation of a set of EUV images of the Sun into spectral irradiance, effectively replacing the malfunctioning MEGS-A instrument onboard SDO. In other words, we generate a virtual irradiance instrument, MEGS-AI, for SDO. Using an Image-to-Image translation tool (ITI), this virtual instrument can also be trained and added on other EUV-observing satellites like STEREO, GOES, SolO, and the upcoming VIGIL satellite, enabling unprecedented irradiance estimates from additional satellite missions. In the case of the STEREO twin-satellites and VIGIL, this enables estimates of spectral irradiance prior to the Sun rotating into Earth’s view, which directly enables the forecast of enhanced irradiance. Additionally, we explore different combinations of images in different EUV channels and evaluate their contributions in estimating different irradiance channels. Finally, when combined with a neural radiance field model of the Sun (SuNeRFs), MEGS-AI can estimate spectral irradiance from any viewpoint in the solar system, enabling for the first time a complete 4pi spectral irradiance map of the Sun. This can be directly used to estimate the Sun’s impact on other planets in the solar system and to determine the total solar irradiance output in multiple EUV spectral bands.