  Cosmological surveys aim at answering fundamental questions about our
Universe, including the nature of dark matter or the reason of unexpected
accelerated expansion of the Universe. In order to answer these questions, two
important ingredients are needed: 1) data from observations and 2) a
theoretical model that allows fast comparison between observation and theory.
Most of the cosmological surveys observe galaxies, which are very difficult to
model theoretically due to the complicated physics involved in their formation
and evolution; modeling realistic galaxies over cosmological volumes requires
running computationally expensive hydrodynamic simulations that can cost
millions of CPU hours. In this paper, we propose to use deep learning to
establish a mapping between the 3D galaxy distribution in hydrodynamic
simulations and its underlying dark matter distribution. One of the major
challenges in this pursuit is the very high sparsity in the predicted galaxy
distribution. To this end, we develop a two-phase convolutional neural network
architecture to generate fast galaxy catalogues, and compare our results
against a standard cosmological technique. We find that our proposed approach
either outperforms or is competitive with traditional cosmological techniques.
Compared to the common methods used in cosmology, our approach also provides a
nice trade-off between time-consumption (comparable to fastest benchmark in the
literature) and the quality and accuracy of the predicted simulation. In
combination with current and upcoming data from cosmological observations, our
method has the potential to answer fundamental questions about our Universe
with the highest accuracy.
