Modelling galaxy clustering: halo occupation distribution versus subhalo matching

Area of Science:

• Cosmology
• Astrophysics
• Galaxy Formation and Evolution

Background:

• Understanding galaxy distribution and clustering is crucial for probing cosmic structure formation.
• The Sloan Digital Sky Survey (SDSS) provides extensive data on galaxy properties and spatial distribution.

Purpose of the Study:

• To model and reproduce the luminosity-dependent projected and redshift-space two-point correlation functions (2PCFs) of galaxies.
• To compare the performance of the halo occupation distribution (HOD) model and subhalo abundance matching (SHAM) models.

Main Methods:

• Utilized the halo occupation distribution (HOD) model and subhalo abundance matching (SHAM) model and its extension (SCAM).
• Employed high-resolution N-body simulations for model construction.
• Jointly fitted galaxy 2PCFs and abundances, considering subhalo properties like accretion mass (Macc), accretion velocity (Vacc), and peak velocity (Vpeak).

Main Results:

• The HOD model generally provided the best fit for galaxy clustering measurements in both projected and redshift spaces.
• The extended SHAM model (SCAM) showed improved performance, especially for low-luminosity galaxies, with the Vacc-selected subhalo model performing best.
• The Macc-selected subhalo model failed to fit low-luminosity galaxy data.

Conclusions:

• The HOD model is a robust tool for reproducing large-scale galaxy clustering.
• The SCAM model offers a flexible framework for galaxy-halo connections, with subhalo properties significantly impacting model performance for different luminosity samples.
• Further investigation into subhalo properties is essential for accurately modeling galaxy populations across all luminosities.