Machine learning on simulated images identifies that flux eruption events cause more diffuse, polarized, lower-flux millimeter emission with decreased Q-U loop rotation rate, achieving ~80% accuracy with random forests on summary statistics.
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A 1D accretion model shows MAD formation for Pm ≳ 1, outer-disk IR emission missed by one-zone approximations, and Pm-dependent X-ray mechanisms that affect IBH detectability in dense clouds.
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Identifying Observational Signatures of Flux Eruption Events in Supermassive Black Hole Accretion Flows with Machine Learning
Machine learning on simulated images identifies that flux eruption events cause more diffuse, polarized, lower-flux millimeter emission with decreased Q-U loop rotation rate, achieving ~80% accuracy with random forests on summary statistics.
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Multi-wavelength Emission Modeling from Accretion Flows around Isolated Black Holes Including Magnetic Flux Transport
A 1D accretion model shows MAD formation for Pm ≳ 1, outer-disk IR emission missed by one-zone approximations, and Pm-dependent X-ray mechanisms that affect IBH detectability in dense clouds.