The galaxy luminosity function and galaxy stellar mass function are fundamental statistics to test the galaxy formation models. Theoretical predictions based on cosmological simulations can deviate from observations, especially at… Click to show full abstract
The galaxy luminosity function and galaxy stellar mass function are fundamental statistics to test the galaxy formation models. Theoretical predictions based on cosmological simulations can deviate from observations, especially at bright and faint ends. In this case, the mismatch may come from missing physics, oversimplified or inaccurate model recipes, or from inappropriate methods of extracting basic astrophysical quantities from simulations. The latter is a crucial aspect to consider to avoid misleading conclusions when comparing simulations with observations. In this paper, we have applied a new method to produce ‘observed’ galaxies identified in mock imaging of hydrodynamical simulations. We have generated low-redshift mock galaxies from the TNG100-1 simulation of IllustrisTNG and analysed them using standard ‘observational’ techniques to extract their main structural parameters. We show that our technique can produce realistic surface brightness distributions of the simulated galaxies, including classical morphological substructures, like spiral arms and bars. In particular, we find a very good agreement of the total luminosity and stellar mass versus halo mass relation and the galaxy stellar mass versus size relation among mock observations and real galaxies. We also compare the luminosity function and the mass function of the mock galaxy sample with literature data and find a good agreement at all luminosity and mass scales. In particular, we find no significant tension of the bright end of the galaxy luminosity function, as reported in many analyses using simplified recipes to identify galaxy haloes, in fact miscounting the contribution of the extended galaxy haloes around large galaxies. This demonstrates the critical impact of using observational driven approaches to the simulation analyses to produce realistic predictions to compare to observations.
               
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