Painting halos from cosmic density fields of dark matter with physically
  motivated neural networks
  We present a novel halo painting network that learns to map approximate 3D
dark matter fields to realistic halo distributions. This map is provided via a
physically motivated network with which we can learn the non-trivial local
relation between dark matter density field and halo distributions without
relying on a physical model. Unlike other generative or regressive models, a
well motivated prior and simple physical principles allow us to train the
mapping network quickly and with relatively little data. In learning to paint
halo distributions from computationally cheap, analytical and non-linear
density fields, we bypass the need for full particle mesh simulations and halo
finding algorithms. Furthermore, by design, our halo painting network needs
only local patches of dark matter density to predict the halos, and as such, it
can predict the 3D halo distribution for any arbitrary simulation box size. Our
neural network can be trained using small simulations and used to predict large
halo distributions, as long as the resolutions are equivalent. We evaluate our
model's ability to generate 3D halo count distributions which reproduce, to a
high degree, summary statistics such as the power spectrum and bispectrum, of
the input or reference realizations.
