The evolving AGN duty cycle in galaxies since z$\sim$3 as encoded in the X-ray luminosity function

Abstract

We present a new modeling of the X-ray luminosity function (XLF) of Active Galactic Nuclei (AGN) out to z$\sim$3, dissecting the contribution of main-sequence (MS) and starburst (SB) galaxies. For each galaxy population, we convolved the observed galaxy stellar mass (M${}_{\star }$) function with a grid of M${}_{\star }$-independent Eddington ratio (${\lambda }_{\mathrm{EDD}}$) distributions, normalised via empirical black hole accretion rate (BHAR) to star formation rate (SFR) relations. Our simple approach yields an excellent agreement with the observed XLF since z$\sim$3. We find that the redshift evolution of the observed XLF can only be reproduced through an intrinsic flattening of the ${\lambda }_{\mathrm{EDD}}$ distribution, and with a positive shift of the break ${\lambda }^{\ast }$, consistent with an anti-hierarchical behavior. The AGN accretion history is predominantly made by massive (10${}^{10}<$M${}_{\star }<$10${}^{11}$ M${}_{\odot }$) MS galaxies, while SB-driven BH accretion, possibly associated with galaxy mergers, becomes dominant only in bright quasars, at $\log$(L${}_{\mathrm{X}}$/erg s${}^{-1}$)$>$44.36 + 1.28$\cdot$(1+z). We infer that the probability of finding highly-accreting (${\lambda }_{\mathrm{EDD}}>$ 10%) AGN significantly increases with redshift, from 0.4% (3.0%) at z=0.5 to 6.5% (15.3%) at z=3 for MS (SB) galaxies, implying a longer AGN duty cycle in the early Universe. Our results strongly favor a M${}_{\star }$-dependent ratio between BHAR and SFR, as BHAR/SFR $\propto$ M${}_{\star }^{0.73[+0.22,-0.29]}$, supporting a non-linear BH buildup relative to the host. Finally, this framework opens potential questions on super-Eddington BH accretion and different ${\lambda }_{\mathrm{EDD}}$ prescriptions for understanding the cosmic BH mass assembly.

arXiv commentAccepted for publication in ApJ. 25 pages, 15 figures, 2 tables. The best X-ray luminosity functions are available at https://github.com/idelvecchio/XLF_Delvecchio2020.git

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