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.
The matter content of the jet in M87: evidence for an electron-positron jet
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abstract
Recent observations have allowed the geometry and kinematics of the M87 jet to be tightly constrained. We combine these constraints with historical Very Long Baseline Interferometry (VLBI) results and the theory of synchrotron self-absorbed radio cores in order to investigate the physical properties of the jet. Our results strongly suggest the jet to be dominated by an electron-positron (pair) plasma. Although our conservative constraints cannot conclusively dismiss an electron-proton plasma, the viability of this solution is extremely vulnerable to further tightening of VLBI surface brightness limits. The arguments presented, coupled with future high-resolution multi-frequency VLBI studies of the jet core, will be able to firmly distinguish these two possibilities.
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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.