dm2gal: Mapping Dark Matter to Galaxies with Neural Networks
  Maps of cosmic structure produced by galaxy surveys are one of the key tools
for answering fundamental questions about the Universe. Accurate theoretical
predictions for these quantities are needed to maximize the scientific return
of these programs. Simulating the Universe by including gravity and
hydrodynamics is one of the most powerful techniques to accomplish this;
unfortunately, these simulations are very expensive computationally.
Alternatively, gravity-only simulations are cheaper, but do not predict the
locations and properties of galaxies in the cosmic web. In this work, we use
convolutional neural networks to paint galaxy stellar masses on top of the dark
matter field generated by gravity-only simulations. Stellar mass of galaxies
are important for galaxy selection in surveys and thus an important quantity
that needs to be predicted. Our model outperforms the state-of-the-art
benchmark model and allows the generation of fast and accurate models of the
observed galaxy distribution.
