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

TL;DR

This paper introduces a CNN-based calibration method for the Atmospheric Imaging Assembly that effectively replaces traditional sounding rocket calibration, improving accuracy and enabling continuous instrument calibration using space-based data.

Contribution

The paper presents a novel machine learning framework for auto-calibrating EUV solar imaging instruments, reducing reliance on infrequent sounding rocket calibrations.

Findings

CNN models closely match sounding rocket calibration results

The approach outperforms traditional astronomer's techniques

Enables continuous calibration of space-based EUV instruments

Abstract

Solar activity plays a quintessential role in influencing the interplanetary medium and space-weather around the Earth. Remote sensing instruments onboard 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) onboard NASA's Solar Dynamics Observatory (SDO), suffer from time-dependent degradation, reducing their sensitivity. Current state-of-the-art calibration techniques rely on periodic sounding rockets, which can be infrequent and rather unfeasible for deep-space missions. We present an alternative calibration approach based on convolutional neural networks (CNNs). We use SDO-AIA data for our analysis. Our results show that CNN-based models could comprehensively reproduce the sounding rocket experiments' outcomes within a reasonable degree of accuracy, indicating that it performs equally well compared with the current techniques. Furthermore, a comparison with a standard "astronomer's technique" baseline model reveals that the CNN approach significantly outperforms this baseline. Our approach establishes the framework for a novel technique to calibrate EUV instruments and advance our understanding of the cross-channel relation between different EUV channels.