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ABSTRACT We present an analysis of the quenching of local observed and simulated galaxies, including an investigation of the dependence of quiescence on both intrinsic and environmental parameters. We apply an advanced machine learning technique utilizing random forest classification to predict when galaxies are star forming or quenched. We perform separate classification analyses for three groups of galaxies: (a) central galaxies, (b) high-mass satellites (M* 10^10. 5\, { M }), and (c) low-mass satellites (M* 10^10\, { M }) for three cosmological hydrodynamical simulations (Evolution and Assembly of GaLaxies and their Environments, Illustris, and IllustrisTNG), and observational data from the Sloan Digital Sky Survey. The simulation results are unanimous and unambiguous: quiescence in centrals and high-mass satellites is best predicted by intrinsic parameters (specifically central black hole mass), while it is best predicted by environmental parameters (specifically halo mass) for low-mass satellites. In observations, we find black hole mass to best predict quiescence for centrals and high-mass satellites, exactly as predicted by the simulations. However, local galaxy overdensity is found to be most predictive parameter for low-mass satellites. None the less, both simulations and observations do agree that it is environment which quenches low-mass satellites. We provide evidence which suggests that the dominance of local overdensity in classifying low-mass systems may be due to the high uncertainty in halo mass estimation from abundance matching, rather than it being fundamentally a more predictive parameter. Finally, we establish that the qualitative trends with environment predicted in simulations are recoverable in the observation space. This has important implications for future wide-field galaxy surveys.
Goubert et al. (Sat,) studied this question.