Auto-Calibration of Remote Sensing Solar Telescopes with Deep Learning

TL;DR

This paper presents a deep learning method using CNNs to auto-calibrate space-based UV and EUV solar instruments, addressing sensitivity degradation without frequent rocket calibrations, thus enabling more autonomous deep space solar observations.

Contribution

The authors develop a CNN-based auto-calibration technique that corrects instrument degradation using spatial patterns in multi-wavelength data, reducing reliance on infrequent sounding rocket calibrations.

Findings

Successfully calibrates SDO/AIA channels using CNNs

Reduces sensitivity degradation effects in UV/EUV solar imaging

Enables autonomous calibration for future deep space missions

Abstract

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