Kavli Affiliate: Roberto Maiolino
| First 5 Authors: Paul H. Goubert, Asa F. L. Bluck, Joanna M. Piotrowska, Roberto Maiolino,
| Summary:
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.
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