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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Halo-driven transient rapid growth followed by thermodynamic suppression explains over-massive black holes and Little Red Dots as precursors to standard SMBH-galaxy coevolution.
Extreme-value statistics applied to JWST high-redshift galaxy data predicts a black hole to stellar mass ratio of approximately 0.24.
Simulations and analytic modeling predict that the supermassive black hole to stellar mass ratio peaks at several percent around redshift 7-10 before declining toward the present day.
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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Halo-driven Origin and Suppression of Over-massive Black Holes and Little Red Dots
Halo-driven transient rapid growth followed by thermodynamic suppression explains over-massive black holes and Little Red Dots as precursors to standard SMBH-galaxy coevolution.
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Extreme Values of Black Hole to Stellar Mass Ratio for High-Redshift Galaxies
Extreme-value statistics applied to JWST high-redshift galaxy data predicts a black hole to stellar mass ratio of approximately 0.24.
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Redshift Evolution of the Ratio of Supermassive Black Hole Mass to Stellar Mass
Simulations and analytic modeling predict that the supermassive black hole to stellar mass ratio peaks at several percent around redshift 7-10 before declining toward the present day.