Galaxy Properties from the Ultra-violet to the Far-Infrared: Lambda-CDM models confront observations

TL;DR

This study combines galaxy formation models with dust physics to predict galaxy luminosity functions across a wide wavelength range and redshift, revealing successes and limitations in matching observations.

Contribution

It introduces an evolving dust-to-metal ratio in galaxy models to better match high-redshift UV and optical luminosity functions.

Findings

Good agreement with observations from UV to far-IR wavelengths.

Underprediction of bright sub-mm galaxies at long wavelengths.

Reproduction of total IR luminosity function aligns with observations.

Abstract

We combine a semi-analytic model of galaxy formation with simple analytic recipes describing the absorption and re-emission of starlight by dust in the interstellar medium of galaxies. We use the resulting models to predict galaxy counts and luminosity functions from the far-ultraviolet to the sub-mm, from redshift five to the present, and compare with an extensive compilation of observations. We find that in order to reproduce the rest-UV and optical luminosity functions at high redshift, we must assume an evolving normalization in the dust-to-metal ratio, implying that galaxies of a given bolometric luminosity (or metal column density) must be less extinguished than their local counterparts. In our best-fit model, we find remarkably good agreement with observations from rest-frame 1500 Angstroms to 250 microns. At longer wavelengths, most dramatically in the sub-mm, our models underpredict the number of bright galaxies by a large factor. The models reproduce the observed total IR luminosity function fairly well. We show the results of varying several ingredients of the models, including various aspects of the dust attenuation recipe, the dust emission templates, and the cosmology. We use our models to predict the integrated Extragalactic Background Light (EBL), and compare with an observationally-motivated EBL model and with other available observational constraints.