Homogenising SoHO/EIT and SDO/AIA 171Å$~$ Images: A Deep Learning
  Approach
Subhamoy Chatterjee Andrés Muñoz-Jaramillo Maher Dayeh Hazel M. Bain Kimberly Moreland
Extreme Ultraviolet 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 dataset. In
this study, we utilize the temporal overlap of SoHO/EIT and SDO/AIA 171~\AA
~surveys to train an ensemble of deep learning models for creating a single
homogeneous survey of EUV images for 2 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.