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.
Constraining Radiatively Inefficient Accretion Flows with Polarization
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
The low-luminosity black hole Sgr A* provides a testbed for models of Radiatively Inefficient Accretion Flows (RIAFs). Recent sub-millimeter linear polarization measurements of Sgr A* have provided evidence that the electrons in the accretion flow are relativistic over a large range of radii. Here, we show that these high temperatures result in elliptical plasma normal modes. Thus, polarized millimeter and sub-millimeter radiation emitted within RIAFs will undergo generalized Faraday rotation, a cyclic conversion between linear and circular polarization. This effect will not depolarize the radiation even if the rotation measure is extremely high. Rather, the beam will take on the linear and circular polarization properties of the plasma normal modes. As a result, polarization measurements of Sgr A* in this frequency regime will constrain the temperature, density and magnetic profiles of RIAF models.
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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.