The BACCO simulation project: biased tracers in real space

TL;DR

This paper introduces a neural network-based emulator for the two-point clustering of biased tracers in real space, calibrated with extensive cosmological models including neutrinos and dark energy, achieving high accuracy on galaxy clustering predictions.

Contribution

The paper presents a novel emulator for biased tracer clustering that incorporates cosmology dependence via rescaling and uses a Lagrangian bias expansion, covering complex cosmologies including neutrinos and dark energy.

Findings

Achieves 1-2% accuracy in power spectrum predictions up to nonlinear scales

Capable of modeling galaxy clustering for surveys at z ~ 1

Utilizes a neural network trained on over 400 cosmological models

Abstract

We present an emulator for the two-point clustering of biased tracers in real space. We construct this emulator using neural networks calibrated with more than $400$ cosmological models in a 8-dimensional cosmological parameter space that includes massive neutrinos an dynamical dark energy. The properties of biased tracers are described via a Lagrangian perturbative bias expansion which is advected to Eulerian space using the displacement field of numerical simulations. The cosmology-dependence is captured thanks to a cosmology-rescaling algorithm. We show that our emulator is capable of describing the power spectrum of galaxy formation simulations for a sample mimicking that of a typical Emission-Line survey at $z \sim 1$ with an accuracy of $1-2\%$ up to nonlinear scales $k \sim 0.7 h \mathrm{Mpc}^{-1}$.