Generative deep fields: arbitrarily sized, random synthetic astronomical
  images through deep learning
  Generative Adversarial Networks (GANs) are a class of artificial neural
network that can produce realistic, but artificial, images that resemble those
in a training set. In typical GAN architectures these images are small, but a
variant known as Spatial-GANs (SGANs) can generate arbitrarily large images,
provided training images exhibit some level of periodicity. Deep extragalactic
imaging surveys meet this criteria due to the cosmological tenet of isotropy.
Here we train an SGAN to generate images resembling the iconic Hubble Space
Telescope eXtreme Deep Field (XDF). We show that the properties of 'galaxies'
in generated images have a high level of fidelity with galaxies in the real XDF
in terms of abundance, morphology, magnitude distributions and colours. As a
demonstration we have generated a 7.6-billion pixel 'generative deep field'
spanning 1.45 degrees. The technique can be generalised to any appropriate
imaging training set, offering a new purely data-driven approach for producing
realistic mock surveys and synthetic data at scale, in astrophysics and beyond.
