  The future astronomical imaging surveys are set to provide precise
constraints on cosmological parameters, such as dark energy. However,
production of synthetic data for these surveys, to test and validate analysis
methods, suffers from a very high computational cost. In particular, generating
mock galaxy catalogs at sufficiently large volume and high resolution will soon
become computationally unreachable. In this paper, we address this problem with
a Deep Generative Model to create robust mock galaxy catalogs that may be used
to test and develop the analysis pipelines of future weak lensing surveys. We
build our model on a custom built Graph Convolutional Networks, by placing each
galaxy on a graph node and then connecting the graphs within each
gravitationally bound system. We train our model on a cosmological simulation
with realistic galaxy populations to capture the 2D and 3D orientations of
galaxies. The samples from the model exhibit comparable statistical properties
to those in the simulations. To the best of our knowledge, this is the first
instance of a generative model on graphs in an astrophysical/cosmological
context.
