An empirical model to form and evolve galaxies in dark matter halos
Shijie Li, Youcai Zhang, Xiaohu Yang, Huiyuan Wang, Dylan Tweed,, Chengze Liu, Lei Yang, Feng Shi, Yi Lu, Wentao Luo, Jianwen Wei

TL;DR
This paper presents an empirical model that uses star formation histories to simulate galaxy growth within dark matter halos, accurately reproducing various observed galaxy properties across different redshifts.
Contribution
The model is simple, data-driven, and effectively predicts galaxy stellar mass functions, luminosity functions, and gas content, matching multiple observational datasets.
Findings
Accurately predicts galaxy stellar mass functions up to redshift 4.
Reproduces observed luminosity functions across multiple bands.
Matches observed HI-to-stellar mass ratios.
Abstract
Based on the star formation histories (SFH) of galaxies in halos of different masses, we develop an empirical model to grow galaxies in dark mattet halos. This model has very few ingredients, any of which can be associated to observational data and thus be efficiently assessed. By applying this model to a very high resolution cosmological -body simulation, we predict a number of galaxy properties that are a very good match to relevant observational data. Namely, for both centrals and satellites, the galaxy stellar mass function (SMF) up to redshift and the conditional stellar mass functions (CSMF) in the local universe are in good agreement with observations. In addition, the 2-point correlation is well predicted in the different stellar mass ranges explored by our model. Furthermore, after applying stellar population synthesis models to our stellar composition as a…
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