RadioGAN - Translations between different radio surveys with generative
  adversarial networks
  Radio surveys are widely used to study active galactic nuclei. Radio
interferometric observations typically trade-off surface brightness sensitivity
for angular resolution. Hence, observations using a wide range of baseline
lengths are required to recover both bright small-scale structures and diffuse
extended emission. We investigate if generative adversarial networks (GANs) can
extract additional information from radio data and might ultimately recover
extended flux from a survey with a high angular resolution and vice versa. We
use a GAN for the image-to-image translation between two different data sets,
namely the Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) and the
NRAO VLA Sky Survey (NVSS) radio surveys. The GAN is trained to generate the
corresponding image cutout from the other survey for a given input. The results
are analyzed with a variety of metrics, including structural similarity as well
as flux and size comparison of the extracted sources. RadioGAN is able to
recover extended flux density within a $20\%$ margin for almost half of the
sources and learns more complex relations between sources in the two surveys
than simply convolving them with a different synthesized beam. RadioGAN is also
able to achieve subbeam resolution by recognizing complicated underlying
structures from unresolved sources. RadioGAN generates over a third of the
sources within a $20\%$ deviation from both original size and flux for the
FIRST to NVSS translation, while for the NVSS to FIRST mapping it achieves
almost $30\%$ within this range.
