The BACCO simulation project: biased tracers in real space

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 8D 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 ~ 1 with an accuracy of $1-2~{{\ \rm per\ cent}}$ up to non-linear scales $k\sim 0.7 h\, {\rm Mpc}^{-1}$.