Github: https://github.com/Smith42/XDF-GAN

Paper: https://arxiv.org/abs/1904.10286

Link to 7.6 billion pixel generated deep field: http://star.herts.ac.uk/~jgeach/gdf.html

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Hi all! We have used an SGAN to generate an artificial astronomical survey that is statistically indistinguishable from the source data. In the example case above we used the Hubble eXtreme Deep Field (XDF, http://xdf.ucolick.org/) as training data.

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We see this method being used to quickly assemble large training sets for classification and instance segmentation of galaxies. In the early stages of a new survey, relatively small amounts of data would be collected, and then expanded to a level useful for training deep learning models. This would allow us to start using deep learning algorithms on new surveys sooner than previously possible.