The Bacco Simulation Project: Bacco Hybrid Lagrangian Bias Expansion Model in Redshift Space

TL;DR

This paper introduces a neural network emulator based on a 2nd-order Lagrangian bias expansion to accurately predict galaxy power spectra in redshift space, enabling detailed cosmological analysis from small-scale structures.

Contribution

The work develops a novel emulator that combines a Lagrangian bias model with neural networks, covering complex cosmological parameters including neutrinos and dark energy.

Findings

Emulator accurately predicts galaxy power spectra in redshift space.

Provides unbiased cosmological constraints from nonlinear scales.

Enables analysis of small-scale large-scale structure data.

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 2nd-order Lagrangian bias expansion that is displaced to Eulerian space using cosmological $N$-body simulations. Redshift space distortions are then imprinted using the non-linear 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 8-dimensional 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 BOSS-CMASS sample down to nonlinear scales ($k\sim0.6$[$h/$Mpc]$^{3}$). This work opens up the possibility of robustly extracting cosmological information from small scales using observations of the large-scale structure of the Universe.