Paper detail

Evaluating and Improving Semi-analytic modeling of Dust in Galaxies based on Radiative Transfer Calculations II: Dust Emission in the Infrared

Interstellar dust grains are responsible for modifying the spectral energy distribution (SED) of galaxies, both absorbing starlight at UV and optical wavelengths and converting this energy into thermal emission in the infrared. The detailed description of these phenomena is of fundamental importance in order to compare the predictions of theoretical models of galaxy formation and evolution with the most recent observations in the infrared region. In this paper we compare the results of GRASIL, a code explicitly solving for the equation of radiative transfer in a dusty medium, with the predictions of a variety of IR template libraries. We employ star formation history samples extracted from the semi-analytical galaxy formation model MORGANA to create libraries of synthetic SEDs from the near- to the far-infrared. We consider model predictions at different redshift ranges to explore any possible influence in the shape and normalization of the SEDs due to the expected evolution of the galaxy properties. We compute the total absorbed starlight predicted by GRASIL at optical wavelengths to statistically compare the synthetic SEDs with the selected IR templates. We show that synthetic SEDs at a given total infrared luminosity are predicted to be systematically different at different redshift and for different properties of the underlying model galaxy. However, we determine spectral regions where the agreement between the results of radiative transfer and IR templates is good in a statistical sense (i.e. in terms of the luminosity functions). Moreover, we highlight some potentially relevant discrepancies between the different approaches, both in the region dominated by PAH emission and at sub-mm wavelengths. These results determine potentially critical issues in the infrared luminosity functions as predicted by semi-analytical models coupled with different IR flux estimators.