The role of environment and AGN feedback in quenching local galaxies: Comparing cosmological hydrodynamical simulations to the SDSS

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 ($M_{*} > 10^{10.5}{\rm M_{\odot}}$); and (c) low-mass satellites ($M_{*} < 10^{10}{\rm M_{\odot}}$) for three cosmological hydrodynamical simulations (EAGLE, Illustris, and IllustrisTNG), and observational data from the SDSS. 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), whilst 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 over-density is found to be most predictive parameter for low-mass satellites. Nonetheless, 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 over-density 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.