Accurate and Efficient Halo-based Galaxy Clustering Modelling with Simulations

TL;DR

This paper presents a high-resolution simulation-based method for accurately and efficiently modeling galaxy clustering, specifically the two-point correlation functions, to improve understanding of galaxy formation and cosmology.

Contribution

The authors introduce a simulation-based approach that tabulates halo information for fast, precise modeling of galaxy clustering, enhancing analysis of large survey data.

Findings

Accurate modeling of galaxy 2PCFs in real and redshift space.

Efficient exploration of parameter space for galaxy-halo models.

Decomposition of redshift-space 2PCF components to study distortions.

Abstract

Small- and intermediate-scale galaxy clustering can be used to establish the galaxy-halo connection to study galaxy formation and evolution and to tighten constraints on cosmological parameters. With the increasing precision of galaxy clustering measurements from ongoing and forthcoming large galaxy surveys, accurate models are required to interpret the data and extract relevant information. We introduce a method based on high-resolution N-body simulations to accurately and efficiently model the galaxy two-point correlation functions (2PCFs) in projected and redshift spaces. The basic idea is to tabulate all information of haloes in the simulations necessary for computing the galaxy 2PCFs within the framework of halo occupation distribution or conditional luminosity function. It is equivalent to populating galaxies to dark matter haloes and using the mock 2PCF measurements as the model predictions. Besides the accurate 2PCF calculations, the method is also fast and therefore enables an efficient exploration of the parameter space. As an example of the method, we decompose the redshift-space galaxy 2PCF into different components based on the type of galaxy pairs and show the redshift-space distortion effect in each component. The generalizations and limitations of the method are discussed.