The bacco simulation project: bacco hybrid Lagrangian bias expansion model in redshift space
Abstract
We present an emulator that accurately predicts the power spectrum of galaxies in redshift space as a function of cosmological parameters. Our emulator is based on a secondorder Lagrangian bias expansion that is displaced to Eulerian space using cosmological Nbody simulations. Redshift space distortions are then imprinted using the nonlinear velocity field of simulated particles and haloes. We build the emulator using a forward neural network trained with the simulations of the BACCO project, which covers an eightdimensional parameter space including massive neutrinos and dynamical dark energy. We show that our emulator provides unbiased cosmological constraints from the monopole, quadrupole, and hexadecapole of a mock galaxy catalogue that mimics the BOSSCMASS sample down to nonlinear scales ($k\sim 0.6{h\, {\rm Mpc}^{1}}$). This work opens up the possibility of robustly extracting cosmological information from small scales using observations of the largescale structure of the universe.
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 April 2023
 DOI:
 10.1093/mnras/stad368
 arXiv:
 arXiv:2207.06437
 Bibcode:
 2023MNRAS.520.3725P
 Keywords:

 cosmology: theory;
 largescale structure of Universe;
 methods: statistical;
 methods: numerical;
 Astrophysics  Cosmology and Nongalactic Astrophysics
 EPrint:
 16 pages, 9 figures