The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: Modeling the clustering and halo occupation distribution of BOSS-CMASS galaxies in the Final Data Release
Sergio A. Rodr\'iguez-Torres, Chia-Hsun Chuang, Francisco Prada, Hong, Guo, Anatoly Klypin, Peter Behroozi, Chang Hoon Hahn, Johan Comparat, Gustavo, Yepes, Antonio D. Montero-Dorta, Joel R. Brownstein, Claudia Maraston,, Cameron K. McBride, Jeremy Tinker, Stefan Gottl\"ober

TL;DR
This paper models the clustering of BOSS CMASS galaxies using Halo Abundance Matching within a large N-body simulation, successfully reproducing observed clustering statistics and providing insights into galaxy-halo connections.
Contribution
It introduces a detailed HAM-based clustering model that accurately matches multiple observed clustering statistics of BOSS CMASS galaxies in a realistic simulation environment.
Findings
Model reproduces observed monopole of 2-point correlation function within 1-$\sigma$
Good agreement between predicted and observed galaxy power spectrum and three-point correlation function
Identifies tensions in the quadrupole, suggesting possible target selection effects
Abstract
We present a study of the clustering and halo occupation distribution of BOSS CMASS galaxies in the redshift range 0.43 < z < 0.7 drawn from the Final SDSS-III Data Release. We compare the BOSS results with the predictions of a Halo Abundance Matching (HAM) clustering model that assigns galaxies to dark matter halos selected from the large BigMultiDark -body simulation of a flat CDM Planck cosmology. We compare the observational data with the simulated ones on a light-cone constructed from 20 subsequent outputs of the simulation. Observational effects such as incompleteness, geometry, veto masks and fiber collisions are included in the model, which reproduces within 1- errors the observed monopole of the 2-point correlation function at all relevant scales: from the smallest scales, 0.5 Mpc, up to scales beyond the Baryonic Acoustic Oscillation feature. This…
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