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We show the significance of the super-Eddington accretion for the cosmic growth of supermassive black holes (SMBHs) with a semi-analytical model for galaxy and black hole evolution. The model explains various observed properties of galaxies and active galactic nuclei at a wide redshift range. By tracing the growth history of individual SMBHs, we find that the fraction of the SMBH mass acquired during the super-Eddington accretion phases to the total SMBH mass becomes larger for less massive black holes and at higher redshift. Even at z = 0, SMBHs with > 1e+9 Msun have acquired more than 50% of their mass by super-Eddington accretions, which is apparently inconsistent with classical Soltans argument. However, the mass-weighted radiation efficiency of SMBHs with > 1e+8 Msun obtained with our model, is about 0.08 at z = 0, which is consistent with Soltans argument within the observational uncertainties. We, therefore, conclude that Soltans argument cannot reject the possibility that SMBHs are grown mainly by super-Eddington accretions.
We present a simple semi-numerical model designed to explore black hole growth and galaxy evolution. This method builds on a previous model for black hole accretion that uses a semi-numerical galaxy formation model and universal Eddington ratio distr
Supermassive black hole (SMBH) binaries residing at the core of merging galaxies are recently found to be strongly affected by the rotation of their host galaxies. The highly eccentric orbits that form when the host is counterrotating emit strong bur
By means of our own cosmological-hydrodynamical simulation and semi-analytical model we studied galaxy population properties in clusters and groups, spanning over 10 different bands from UV to NIR, and their evolution since redshift z=2. We compare o
The co-evolution of supermassive black holes (SMBHs) with their host galaxies remains to be fully explored, especially at high redshift. While often understood as a consequence of self-regulation via AGN feedback, it may also be explained by alternat
We implement a sample-efficient method for rapid and accurate emulation of semi-analytical galaxy formation models over a wide range of model outputs. We use ensembled deep learning algorithms to produce a fast emulator of an updated version of the G