Abstract

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. Over the past two decades multiple mechanisms were suggested as potential drivers of the transition between the star-forming and quiescent galaxy categories, referred to as galaxy ‘quenching’.

In this talk I will present the results of our recent study, in which we combine machine learning with partial correlation analysis to determine which among the three potential quenching mechanisms: supernova feedback, halo shock heating or AGN feedback are most likely responsible for bringing star formation to a halt in massive, central galaxies. To this end we bridge the gap between theory and observation by extracting theoretical predictions from three state-of-the-art cosmological simulations – EAGLE , Illustris and IllustrisTNG and comparing them with the Sloan Digital Sky Survey (SDSS) observations. We find that the supermassive black hole mass (MBH) is the most powerful parameter in determining whether a galaxy is star-forming or quenched across all datasets. Remarkably, this result is true for all different implementations of AGN feedback in the simulations and is met overwhelmingly well in the SDSS , where we infer MBH from a variety of calibrations for ~230 000 local galaxies.

In my brief talk I will share our results together with our methodology to make a convincing case for star formation being quenched by AGN in massive, central galaxies. If you would like to learn more about this study, you can now read it on arXiv/MNRAS at the following link: https://arxiv.org/abs/2112.07672