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 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 $k \sim 0.7 h \mathrm{Mpc}^{-1}$.
