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

Abstract

Context. Solar activity plays a quintessential role in influencing the interplanetary medium and space-weather around Earth. Remote sensing instruments on-board heliophysics space missions provide a pool of information about the Sun’s activity, via the measurement of its magnetic field and the emission of light from the multi-layered, multi-thermal, and dynamic solar atmosphere. Extreme UV (EUV) wavelength observations from space help in understanding the subtleties of the outer layers of the Sun, namely the chromosphere and the corona. Unfortunately, such instruments, like the Atmospheric Imaging Assembly (AIA) on-board NASA’s Solar Dynamics Observatory (SDO), suffer from time-dependent degradation that reduces their sensitivity. Current state-of-the-art calibration techniques rely on sounding rocket flights to maintain absolute calibration, which are infrequent, complex, and limited to a single vantage point. Aims. We aim to develop a novel method based on machine learning (ML) that exploits spatial patterns on the solar surface across multi-wavelength observations to auto-calibrate the instrument degradation. Methods. We establish two convolutional neural network (CNN) architectures that take either single-channel or multi-channel input and train the models using the SDOML dataset. The dataset is further augmented by randomly degrading images at each epoch with the training dataset spanning non-overlapping months with the test dataset. We also develop a non-ML baseline model to assess the gain of the CNN models. With the best trained models, we reconstruct the AIA multi-channel degradation curves of 2010–2020 and compare them with the sounding-rocket based degradation curves. Results. Our results indicate that the CNN-based models significantly outperform the non-ML baseline model in calibrating instrument degradation. Moreover, multi-channel CNN outperforms the single-channel CNN, which suggests the importance of crosschannel relations between different EUV channels for recovering the degradation profiles. The CNN-based models reproduce the degradation corrections derived from the sounding rocket cross-calibration measurements within the experimental measurement uncertainty, indicating that it performs equally well when compared with the current techniques. Conclusions. Our approach establishes the framework for a novel technique based on CNNs to calibrate EUV instruments. We envision that this technique can be adapted to other imaging or spectral instruments operating at other wavelengths.