DESI DR1 yields 314k high-mass and 9.6k dwarf AGN, extending the M_BH-M_star relation to log M_star ~7.8 and suggesting two evolutionary pathways for galaxies and black holes.
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Machine learning on cosmological simulations achieves 91-94% accuracy classifying over-massive versus under-massive SMBH growth regimes from LSST photometry, with 83-89% cross-simulation transfer accuracy driven primarily by host galaxy colors.
Black hole occupation fraction rises with stellar mass but its normalization, shape, and redshift trend depend strongly on BH mass threshold, central vs satellite galaxies, simulation box, resolution, and sampled population in the FEGA25 model.
Simulations show heavy-seed formation with BH-BH mergers or Bondi accretion under weak feedback can assemble 10^6-10^7 solar mass black holes at z greater than or equal to 9.
citing papers explorer
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Classifying Supermassive Black Hole Growth Regimes to Observables Across Cosmological Simulations with Forecasts for LSST
Machine learning on cosmological simulations achieves 91-94% accuracy classifying over-massive versus under-massive SMBH growth regimes from LSST photometry, with 83-89% cross-simulation transfer accuracy driven primarily by host galaxy colors.
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Black Hole Occupation Fraction: Dependence on Black Hole Mass Threshold, Environment, Resolution and Redshift
Black hole occupation fraction rises with stellar mass but its normalization, shape, and redshift trend depend strongly on BH mass threshold, central vs satellite galaxies, simulation box, resolution, and sampled population in the FEGA25 model.
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Learning the Universe at High Redshifts: Impact of Accretion Modeling on Early Black Hole Growth
Simulations show heavy-seed formation with BH-BH mergers or Bondi accretion under weak feedback can assemble 10^6-10^7 solar mass black holes at z greater than or equal to 9.