  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
