# A volumetric deep Convolutional Neural Network for simulation of dark matter halo catalogues

@article{Berger2018AVD, title={A volumetric deep Convolutional Neural Network for simulation of dark matter halo catalogues}, author={P. Berger and G. Stein}, journal={ArXiv}, year={2018}, volume={abs/1805.04537} }

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… Expand

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