  The Solar Dynamics Observatory (SDO), a NASA multi-spectral decade-long
mission that has been daily producing terabytes of observational data from the
Sun, has been recently used as a use-case to demonstrate the potential of
machine learning methodologies and to pave the way for future deep-space
mission planning. In particular, the idea of using image-to-image translation
to virtually produce extreme ultra-violet channels has been proposed in several
recent studies, as a way to both enhance missions with less available channels
and to alleviate the challenges due to the low downlink rate in deep space.
This paper investigates the potential and the limitations of such a deep
learning approach by focusing on the permutation of four channels and an
encoder--decoder based architecture, with particular attention to how
morphological traits and brightness of the solar surface affect the neural
network predictions. In this work we want to answer the question: can synthetic
images of the solar corona produced via image-to-image translation be used for
scientific studies of the Sun? The analysis highlights that the neural network
produces high-quality images over three orders of magnitude in count rate
(pixel intensity) and can generally reproduce the covariance across channels
within a 1% error. However the model performance drastically diminishes in
correspondence of extremely high energetic events like flares, and we argue
that the reason is related to the rareness of such events posing a challenge to
model training.
