Perturbation theory for modeling galaxy bias: validation with simulations of the Dark Energy Survey

TL;DR

This paper develops and validates a perturbation theory model for galaxy bias using simulations tailored for the Dark Energy Survey, achieving accurate correlation function predictions above 4 Mpc/h scales.

Contribution

The paper introduces a five-parameter effective perturbation theory model for galaxy bias that is validated against DES mock catalogs, demonstrating high accuracy on relevant scales.

Findings

Model describes correlation functions within 2% accuracy.

Two bias parameters can be fixed based on co-evolution assumptions.

Using the full non-linear matter power spectrum improves model fit.

Abstract

We describe perturbation theory (PT) models of galaxy bias for applications to photometric galaxy surveys. We model the galaxy-galaxy and galaxy-matter correlation functions in configuration space and validate against measurements from mock catalogs designed for the Dark Energy Survey (DES). We find that an effective PT model with five galaxy bias parameters provides a good description of the 3D correlation functions above scales of 4 Mpc/$h$ and $z < 1$. Our tests show that at the projected precision of the DES-Year 3 analysis, two of the non-linear bias parameters can be fixed to their co-evolution values, and a third (the $k^2$ term for higher derivative bias) set to zero. The agreement is typically at the 2 percent level over scales of interest, which is the statistical uncertainty of our simulation measurements. To achieve this level of agreement, our {\it fiducial} model requires using the full non-linear matter power spectrum (rather than the 1-loop PT one). We also measure the relationship between the non-linear and linear bias parameters and compare them to their expected co-evolution values. We use these tests to motivate the galaxy bias model and scale cuts for the cosmological analysis of the Dark Energy Survey; our conclusions are generally applicable to all photometric surveys.