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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ARTEMIS and EAGLE simulations classify L* galaxies by central BH-to-stellar-mass ratio and trace how merger history drives divergence in BH growth, star formation, and morphology, offering an explanation for the observed scatter and for MW/M31 differences.
A new halo occupation model called HOMe reproduces the anisotropic clustering of ELGs and LRGs down to 200 h^{-1} kpc scales by sampling satellites from dark matter particle positions and fitting parameters to two-point statistics.
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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Co-evolution of Supermassive Black Holes and their Host L* galaxies: implications for Milky Way and M31
ARTEMIS and EAGLE simulations classify L* galaxies by central BH-to-stellar-mass ratio and trace how merger history drives divergence in BH growth, star formation, and morphology, offering an explanation for the observed scatter and for MW/M31 differences.
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ELG$\times$LRG distribution through dark matter halo dynamics
A new halo occupation model called HOMe reproduces the anisotropic clustering of ELGs and LRGs down to 200 h^{-1} kpc scales by sampling satellites from dark matter particle positions and fitting parameters to two-point statistics.