Black Hole and Galaxy Coevolution from Continuity Equation and Abundance Matching
R. Aversa (1,4), A. Lapi (1,2,3,4), G. de Zotti (1,5), F. Shankar (6),, L. Danese (1,3,4) (1-SISSA, Trieste, Italy, 2-Univ. `Tor Vergata', Roma,, Italy, 3-INAF/OATS, Trieste, Italy, 4-INFN/TS, Trieste, Italy, 5-INAF/OAPD,, Padova, Italy, 6-Univ. of Southampton, Southampton, UK)

TL;DR
This paper uses a statistical approach combining the continuity equation and abundance matching to study the coevolution of galaxies and supermassive black holes, reproducing observed properties and revealing in-situ growth dominance.
Contribution
It presents analytical solutions for the black hole mass function from AGN luminosity functions and introduces an improved abundance matching method linking stellar, black hole, and dark matter components.
Findings
Reproduces local black hole mass function and AGN duty cycle.
Shows in-situ processes dominate star and black hole buildup.
Clustering properties match observations, validating the approach.
Abstract
[abridged] We investigate the coevolution of galaxies and hosted supermassive black holes throughout the history of the Universe by a statistical approach based on the continuity equation and the abundance matching technique. Specifically, we present analytical solutions of the continuity equation without source term to reconstruct the supermassive black hole (BH) mass function from the AGN luminosity functions. Such an approach includes physically-motivated AGN lightcurves tested on independent datasets, which describe the evolution of the Eddington ratio and radiative efficiency from slim- to thin-disc conditions. We nicely reproduce the local estimates of the BH mass function, the AGN duty cycle as a function of mass and redshift, along with the Eddington ratio function and the fraction of galaxies with given stellar mass hosting an AGN with given Eddington ratio. We exploit the same…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
