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 protohaloes 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 \sim 92 \% in only 24 hours of training .
We present a simple and fast geometric halo finding algorithm to extract haloes 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 \sim 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 .