  Astronomy of the 21st century increasingly finds itself with extreme
quantities of data. This growth in data is ripe for modern technologies such as
deep image processing, which has the potential to allow astronomers to
automatically identify, classify, segment and deblend various astronomical
objects. In this paper, we explore the use of chained generative adversarial
networks (GANs), a class of generative models that learn mappings from latent
spaces 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 measurements
for weak lensing by augmenting data with synthetic images. By measuring the
distributions of multiple physical properties, we show that images generated
with our approach closely follow the distributions of real galaxies, further
establishing state-of-the-art GAN architectures as a valuable tool for
modern-day astronomy.
