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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VLBA multi-frequency imaging reveals a relativistic, well-collimated jet of ~745 pc in a z=3.4 super-Eddington radio-loud quasar, distinct from low-redshift analogues.
A new public relativistic transfer-function model reltrans for X-ray reverberation mapping that fits both spectra and lags to measure black-hole masses.
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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A public relativistic transfer function model for X-ray reverberation mapping of accreting black holes
A new public relativistic transfer-function model reltrans for X-ray reverberation mapping that fits both spectra and lags to measure black-hole masses.
- The Effects of Complex Accretion Disk Geometry on Broadened Iron K$\alpha$ Lines
- QPOs from the Viscous Transonic Accretion Flow Around a Spinning Black Hole