The BACCO simulation project: a baryonification emulator with neural networks
Abstract
We present a neural network emulator for baryonic effects in the nonlinear matter power spectrum. We calibrate this emulator using more than 50 000 measurements in a 15D parameter space, varying cosmology and baryonic physics. Baryonic physics is described through a baryonification algorithm, which has been shown to accurately capture the relevant effects on the power spectrum and bispectrum in stateoftheart hydrodynamical simulations. Cosmological parameters are sampled using a cosmologyrescaling 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 $2\!\!3{{\ \rm per\ cent}}$, at scales $k \lt 5 \, h\, {\rm Mpc}^{1}$ and redshifts 0 < z < 1.5. We obtain an accuracy of $1\!\!2{{\ \rm per\ cent}}$, when testing the emulator against a collection of 74 different cosmological hydrodynamical simulations and their respective gravityonly counterparts. We also show that only one baryonic parameter, namely M_{c}, which sets the gas fraction retained per halo mass, is enough to have accurate predictions of most of the baryonic feedbacks at a given epoch. Our emulator is publicly available at http://www.dipc.org/bacco.
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 September 2021
 DOI:
 10.1093/mnras/stab1911
 arXiv:
 arXiv:2011.15018
 Bibcode:
 2021MNRAS.506.4070A
 Keywords:

 methods: numerical;
 cosmological parameters;
 largescale structure of Universe;
 Astrophysics  Cosmology and Nongalactic Astrophysics;
 Astrophysics  Astrophysics of Galaxies
 EPrint:
 13 pages, 11 figures. Comments are welcome