Homogenizing SOHO/EIT and SDO/AIA 171 Å Images: A Deep-learning Approach

Abstract

Extreme-ultraviolet (EUV) images of the Sun are becoming an integral part of space weather prediction tasks. However, having different surveys requires the development of instrument-specific prediction algorithms. As an alternative, it is possible to combine multiple surveys to create a homogeneous data set. In this study, we utilize the temporal overlap of Solar and Heliospheric Observatory Extreme ultraviolet Imaging Telescope and Solar Dynamics Observatory Atmospheric Imaging Assembly 171 Å surveys to train an ensemble of deep-learning models for creating a single homogeneous survey of EUV images for two solar cycles. Prior applications of deep learning have focused on validating the homogeneity of the output while overlooking the systematic estimation of uncertainty. We use an approach called “approximate Bayesian ensembling” to generate an ensemble of models whose uncertainty mimics that of a fully Bayesian neural network at a fraction of the cost. We find that ensemble uncertainty goes down as the training set size increases. Additionally, we show that the model ensemble adds immense value to the prediction by showing higher uncertainty in test data that are not well represented in the training data.