  Understanding and monitoring the complex and dynamic processes of the Sun is
important for a number of human activities on Earth and in space. For this
reason, NASA's Solar Dynamics Observatory (SDO) has been continuously
monitoring the multi-layered Sun's atmosphere in high-resolution since its
launch in 2010, generating terabytes of observational data every day. The
synergy between machine learning and this enormous amount of data has the
potential, still largely unexploited, to advance our understanding of the Sun
and extend the capabilities of heliophysics missions. In the present work, we
show that deep learning applied to SDO data can be successfully used to create
a high-fidelity virtual telescope that generates synthetic observations of the
solar corona by image translation. Towards this end we developed a deep neural
network, structured as an encoder-decoder with skip connections (U-Net), that
reconstructs the Sun's image of one instrument channel given temporally aligned
images in three other channels. The approach we present has the potential to
reduce the telemetry needs of SDO, enhance the capabilities of missions that
have less observing channels, and transform the concept development of future
missions.
