The BACCO simulation project: biased tracers in real space

Abstract

We present an emulator for the two-point clustering of biased tracers in real space. We construct this emulator using neural networks calibrated with more than $400$ cosmological models in a 8-dimensional cosmological parameter space that includes massive neutrinos an dynamical dark energy. The properties of biased tracers are described via a Lagrangian perturbative bias expansion which is advected to Eulerian space using the displacement field of numerical simulations. The cosmology-dependence is captured thanks to a cosmology-rescaling algorithm. We show that our emulator is capable of describing the power spectrum of galaxy formation simulations for a sample mimicking that of a typical Emission-Line survey at $z\sim 1$ with an accuracy of $1-2\%$ up to nonlinear scales $k\sim 0.7h{\mathrm{Mpc}}^{-1}$.

14 pages, 8 figures

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