Kavli Affiliate: Yingjie Peng
| First 5 Authors: Joanna M. Piotrowska, Asa F. L. Bluck, Roberto Maiolino, Yingjie Peng,
| Summary:
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.
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