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We present a novel halo painting network that learns to map approximate 3Ddark matter fields to realistic halo distributions. This map is provided via aphysically motivated network with which we can learn the non-trivial localrelation between dark matter density field and halo distributions withoutrelying on a physical model. Unlike other generative or regressive models, awell motivated prior and simple physical principles allow us to train themapping network quickly and with relatively little data. In learning to painthalo distributions from computationally cheap, analytical and non-lineardensity fields, we bypass the need for full particle mesh simulations and halofinding algorithms. Furthermore, by design, our halo painting network needsonly local patches of dark matter density to predict the halos, and as such, itcan predict the 3D halo distribution for any arbitrary simulation box size. Ourneural network can be trained using small simulations and used to predict largehalo distributions, as long as the resolutions are equivalent. We evaluate ourmodel's ability to generate 3D halo count distributions which reproduce, to ahigh degree, summary statistics such as the power spectrum and bispectrum, ofthe input or reference realizations.

Painting halos from cosmic density fields of dark matter with physically motivated neural networks