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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2 Pith papers cite this work. Polarity classification is still indexing.
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astro-ph.HE 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Simulations show the singularity-free Kerr-Hayward metric yields EHT observables that are functionally indistinguishable from the Kerr metric.
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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On the observational distinguishability of the Kerr and Kerr-Hayward metrics to EHT
Simulations show the singularity-free Kerr-Hayward metric yields EHT observables that are functionally indistinguishable from the Kerr metric.