  We present a neural-network emulator for baryonic effects in the non-linear
matter power spectrum. We calibrate this emulator using more than 50,000
measurements in a 15-dimensional parameters space, varying cosmology and
baryonic physics. Baryonic physics is described through a baryonification
algorithm, that has been shown to accurately capture the relevant effects on
the power spectrum and bispectrum in state-of-the-art hydrodynamical
simulations. Cosmological parameters are sampled using a cosmology-rescaling
approach including massive neutrinos and dynamical dark energy. The specific
quantity we emulate is the ratio between matter power spectrum with baryons and
gravity-only, and we estimate the overall precision of the emulator to be 1-2%,
at all scales 0.01 < k < 5 h/Mpc, and redshifts 0 < z < 1.5. We also obtain an
accuracy of 1-2%, when testing the emulator against a collection of 74
different cosmological hydrodynamical simulations and their respective
gravity-only counterparts. We show also that only one baryonic parameter,
namely Mc, which set the gas fraction retained per halo mass, is enough to have
accurate and realistic predictions of the baryonic feedback at a given epoch.
Our emulator will become publicly available in http://www.dipc.org/bacco.
