A volumetric deep Convolutional Neural Network for simulation of mock
  dark matter halo catalogues
  For modern large-scale structure survey techniques it has become standard
practice to test data analysis pipelines on large suites of mock simulations, a
task which is currently prohibitively expensive for full N-body simulations.
Instead of calculating this costly gravitational evolution, we have trained a
three-dimensional deep Convolutional Neural Network (CNN) to identify dark
matter protohalos directly from the cosmological initial conditions. Training
on halo catalogues from the Peak Patch semi-analytic code, we test various CNN
architectures and find they generically achieve a Dice coefficient of ~92% in
only 24 hours of training. We present a simple and fast geometric halo finding
algorithm to extract halos from this powerful pixel-wise binary classifier and
find that the predicted catalogues match the mass function and power spectra of
the ground truth simulations to within ~10%. We investigate the effect of
long-range tidal forces on an object-by-object basis and find that the
network's predictions are consistent with the non-linear ellipsoidal collapse
equations used explicitly by the Peak Patch algorithm.
