The BACCO Simulation Project: A baryonification emulator with Neural Networks
Abstract
We present a neuralnetwork emulator for baryonic effects in the nonlinear matter power spectrum. We calibrate this emulator using more than 50,000 measurements in a 15dimensional 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 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 gravityonly, and we estimate the overall precision of the emulator to be 12%, at all scales 0.01 < k < 5 h/Mpc, and redshifts 0 < z < 1.5. We also obtain an accuracy of 12%, when testing the emulator against a collection of 74 different cosmological hydrodynamical simulations and their respective gravityonly 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.
 Publication:

arXiv eprints
 Pub Date:
 November 2020
 arXiv:
 arXiv:2011.15018
 Bibcode:
 2020arXiv201115018A
 Keywords:

 Astrophysics  Cosmology and Nongalactic Astrophysics;
 Astrophysics  Astrophysics of Galaxies
 EPrint:
 13 pages, 11 figures. Comments are welcome