Auto-Calibration of Remote Sensing Solar Telescopes with Deep Learning
This solves the calibration issue for heliophysics missions in deep space, allowing continuous solar monitoring from new vantage points, though it is an incremental application of deep learning to a specific domain problem.
The paper tackles the problem of time-dependent sensitivity degradation in ultraviolet and extreme ultraviolet instruments on solar telescopes, such as NASA's SDO/AIA, by developing a Convolutional Neural Network that auto-calibrates these channels using spatial patterns in multi-wavelength observations, enabling future deep-space missions without reliance on infrequent sounding rockets.
As a part of NASA's Heliophysics System Observatory (HSO) fleet of satellites,the Solar Dynamics Observatory (SDO) has continuously monitored the Sun since2010. Ultraviolet (UV) and Extreme UV (EUV) instruments in orbit, such asSDO's Atmospheric Imaging Assembly (AIA) instrument, suffer time-dependent degradation which reduces instrument sensitivity. Accurate calibration for (E)UV instruments currently depends on periodic sounding rockets, which are infrequent and not practical for heliophysics missions in deep space. In the present work, we develop a Convolutional Neural Network (CNN) that auto-calibrates SDO/AIA channels and corrects sensitivity degradation by exploiting spatial patterns in multi-wavelength observations to arrive at a self-calibration of (E)UV imaging instruments. Our results remove a major impediment to developing future HSOmissions of the same scientific caliber as SDO but in deep space, able to observe the Sun from more vantage points than just SDO's current geosynchronous orbit.This approach can be adopted to perform autocalibration of other imaging systems exhibiting similar forms of degradation