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Solar activity plays a quintessential role in influencing the interplanetarymedium and space-weather around the Earth. Remote sensing instruments onboardheliophysics space missions provide a pool of information about the Sun'sactivity via the measurement of its magnetic field and the emission of lightfrom the multi-layered, multi-thermal, and dynamic solar atmosphere. Extreme UV(EUV) wavelength observations from space help in understanding the subtletiesof the outer layers of the Sun, namely the chromosphere and the corona.Unfortunately, such instruments, like the Atmospheric Imaging Assembly (AIA)onboard NASA's Solar Dynamics Observatory (SDO), suffer from time-dependentdegradation, reducing their sensitivity. Current state-of-the-art calibrationtechniques rely on periodic sounding rockets, which can be infrequent andrather unfeasible for deep-space missions. We present an alternativecalibration approach based on convolutional neural networks (CNNs). We useSDO-AIA data for our analysis. Our results show that CNN-based models couldcomprehensively reproduce the sounding rocket experiments' outcomes within areasonable degree of accuracy, indicating that it performs equally wellcompared with the current techniques. Furthermore, a comparison with a standard"astronomer's technique" baseline model reveals that the CNN approachsignificantly outperforms this baseline. Our approach establishes the frameworkfor a novel technique to calibrate EUV instruments and advance ourunderstanding of the cross-channel relation between different EUV channels.

Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning