Details

Autor(en) / Beteiligte

• Smith, Michael J
• Geach, James E

Titel

Generative deep fields: arbitrarily sized, random synthetic astronomical images through deep learning

Ist Teil von

• Monthly notices of the Royal Astronomical Society, 2019-12, Vol.490 (4), p.4985-4990

Erscheinungsjahr

2019

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• ABSTRACT 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 deg. The technique can be generalized 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.