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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LILA can detect IMBH binaries at redshifts 20-30, IMRIs, and provide months-to-years early warnings with high-SNR events for gravity tests.
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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 Binary Detection Landscape for the Laser Interferometer Lunar Antenna (LILA): Signal-to-Noise Calculations & Science Cases
LILA can detect IMBH binaries at redshifts 20-30, IMRIs, and provide months-to-years early warnings with high-SNR events for gravity tests.