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
citation-role summary
background 1
citation-polarity summary
fields
astro-ph.HE 3years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
Derives B*(d) and N*(d) for AGN jet cores from self-absorbed synchrotron emission model with power-law assumptions on Doppler factor, Lorentz factor, B, and N versus jet width d.
Simulations of accreting black holes in standard and complex spacetimes indicate that magnetic geometry, quantum corrections, and binary dynamics influence flares, precession, photon rings, and multi-wavelength variability, with potential EHT constraints.
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.
-
Magnetic field and plasma number density from radio and millimeter core measurements in AGN jets
Derives B*(d) and N*(d) for AGN jet cores from self-absorbed synchrotron emission model with power-law assumptions on Doppler factor, Lorentz factor, B, and N versus jet width d.
-
GRMHD and GRRT Simulations of Black Hole Accretion: Flares, Precession, and Complex Spacetimes
Simulations of accreting black holes in standard and complex spacetimes indicate that magnetic geometry, quantum corrections, and binary dynamics influence flares, precession, photon rings, and multi-wavelength variability, with potential EHT constraints.