Tuesday

Understanding the physical processes responsible for ceasing star formation in galaxies is one of the most important unresolved questions in the field of galaxy evolution. We investigate how star formation is brought to a halt in local, massive, central galaxies by comparing Sloan Digital Sky Survey (SDSS) observations with three state-of-the-art hydrodynamical cosmological simulations – EAGLE, Illustris and IllustrisTNG. We address the complex nature of quenching by combining machine learning techniques with partial correlation analysis to determine which galactic property is the most predictive of quenching. We find that the supermassive black hole mass (MBH) is the most powerful parameter in determining whether a galaxy is star-forming or quenched – a statement which is true for all three implementations of AGN feedback in the simulations. Remarkably, this prediction is precisely confirmed in the SDSS observations, where we infer MBH from a variety of calibrations for ~230 000 local galaxies.

We further show that the observed correlations between quiescence and parameters like stellar mass, halo mass or black hole accretion rate result from the connection between these properties and MBH. Moreover, we find that the black hole accretion rate (which linearly translates to AGN luminosity) is almost irrelevant for quiescence, explaining why observations of optically selected AGN struggle to find evidence for AGN feedback driven quenching in action.

Finally, we infer molecular gas masses from the reddening of the SDSS spectra to characterise the mode of operation of the AGN feedback. We find that both gas fraction and star formation efficiency (SFE) decrease upon transition to quiescence,
a trend predicted well by IllustrisTNG. In contrast, the Illustris feedback model causes SFE to increase, disagreeing with the observations. We conclude that black holes can successfully quench star formation through a combination of heating and turbulence injection into the interstellar medium.