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Extensions to the halo occupation distribution model for more accurate clustering predictions

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arxiv 1906.04298 v1 pith:6UTZDNEO submitted 2019-06-10 astro-ph.CO

classification astro-ph.CO
keywords distributionmodelsamplesclusteringgalaxiesgalaxyhalomock
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We test different implementations of the halo occupation distribution (HOD) model to reconstruct the spatial distribution of galaxies as predicted by a publicly available semi-analytical model (SAM). We compare the measured two-point correlation functions of the HOD mock catalogues and the SAM samples to quantify the fidelity of the reconstruction. We use fixed number density galaxy samples selected according to stellar mass or star formation rate (SFR). We develop three different schemes to populate haloes with galaxies with increasing complexity, considering the scatter of the satellite HOD as an additional parameter in the modelling. We first modify the SAM output, removing assembly bias and using a standard Navarro-Frenk-White density profile for the satellite galaxies as the target to reproduce with our HOD mocks. We find that all models give similar reproductions of the two-halo contribution to the clustering signal, but there are differences in the one-halo term. In particular, the HOD mock reproductions work equally well using either the HOD of central and satellites separately or using a model that also accounts for whether or not the haloes contain a central galaxy. We find that the HOD scatter does not have an important impact on the clustering predictions for stellar mass selected samples. For SFR selections, we obtain the most accurate results assuming a negative binomial distribution for the number of satellites in a halo. The scatter in the satellites HOD is a key consideration for HOD mock catalogues that mimic ELG or SFR selected samples in future galaxy surveys.

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    astro-ph.GA 2019-08 conditional novelty 5.0 of 10

    Using average rather than instantaneous star formation rates reproduces [OII] luminosities within 5% for typical simulated galaxies, and large-scale clustering is robust to the luminosity estimation method.

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