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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GRMHD simulations at spins 0.9375 and 0.998 yield similar fluid properties and full-Stokes EHT images, indicating prior lower-spin runs remain representative for a ≳ 0.9375.
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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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Observational Properties of Near-Maximally Spinning Supermassive Black Holes
GRMHD simulations at spins 0.9375 and 0.998 yield similar fluid properties and full-Stokes EHT images, indicating prior lower-spin runs remain representative for a ≳ 0.9375.
- Circular polarization images of Sgr A* for different magnetic field geometries