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.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
astro-ph.HE 3years
2026 3representative citing papers
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*.
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.
citing papers explorer
-
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.
-
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*.
-
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.