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Astronomy of the 21st century increasingly finds itself with extremequantities of data. This growth in data is ripe for modern technologies such asdeep image processing, which has the potential to allow astronomers toautomatically identify, classify, segment and deblend various astronomicalobjects. In this paper, we explore the use of chained generative adversarialnetworks (GANs), a class of generative models that learn mappings from latentspaces to data distributions by modelling the joint distribution of the data,to produce physically realistic galaxy images as one use case of such models.In cosmology, such datasets can aid in the calibration of shape measurementsfor weak lensing by augmenting data with synthetic images. By measuring thedistributions of multiple physical properties, we show that images generatedwith our approach closely follow the distributions of real galaxies, furtherestablishing state-of-the-art GAN architectures as a valuable tool formodern-day astronomy.

Forging new worlds: high-resolution synthetic galaxies with chained generative adversarial networks