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Auto−Calibration of Remote Sensing Solar Telescopes with Deep Learning

Brad Neuberg‚ Souvik Bose‚ Valentina Salvatelli‚ Luiz F. Guedes dos Santos‚ Mark Cheung‚ Miho Janvier‚ Atılım Güneş Baydin‚ Yarin Gal and Meng Jin

Abstract

As a part of NASA's Heliophysics System Observatory (HSO) fleet of satellites, the Solar Dynamics Observatory (SDO) has continuously monitored the Sun since 2010. Ultraviolet (UV) and Extreme UV (EUV) instruments in orbit, such as SDO'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 HSO missions 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.

Book Title
Second Workshop on Machine Learning and the Physical Sciences (NeurIPS 2019)‚ Vancouver‚ Canada
Year
2019