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 models show PA1 aligns with the approaching limb for high spins, enabling mild disfavoring of low spins and strong disfavoring of Earth-pointing spins in M87* from EHT data, with similar potential for Sgr A*.
citing papers explorer
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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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Ring Position Angles and Spin in M87* and Sgr A*
GRMHD models show PA1 aligns with the approaching limb for high spins, enabling mild disfavoring of low spins and strong disfavoring of Earth-pointing spins in M87* from EHT data, with similar potential for Sgr A*.