On the quenching of star formation in observed and simulated central galaxies: evidence for the role of integrated AGN feedback

Abstract

In this paper we investigate how massive central galaxies cease their star formation by comparing theoretical predictions from cosmological simulations: EAGLE, Illustris and IllustrisTNG with observations of the local Universe from the Sloan Digital Sky Survey (SDSS). Our machine learning (ML) classification reveals supermassive black hole mass ($M_{\rm BH}$) as the most predictive parameter in determining whether a galaxy is star forming or quenched at redshift $z=0$ in all three simulations. This predicted consequence of active galactic nucleus (AGN) quenching is reflected in the observations, where it is true for a range of indirect estimates of $M_{\rm BH}$ via proxies as well as its dynamical measurements. Our partial correlation analysis shows that other galactic parameters lose their strong association with quiescence, once their correlations with $M_{\rm BH}$ are accounted for. In simulations we demonstrate that it is the integrated power output of the AGN, rather than its instantaneous activity, which causes galaxies to quench. Finally, we analyse the change in molecular gas content of galaxies from star forming to passive populations. We find that both gas fractions ($f_{\rm gas}$) and star formation efficiencies (SFEs) decrease upon transition to quiescence in the observations but SFE is more predictive than $f_{\rm gas}$ in the ML passive/star-forming classification. These trends in the SDSS are most closely recovered in IllustrisTNG and are in direct contrast with the predictions made by Illustris. We conclude that a viable AGN feedback prescription can be achieved by a combination of preventative feedback and turbulence injection which together quench star formation in central galaxies.