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.
year = 1998, month = oct, volume =
2 Pith papers cite this work. Polarity classification is still indexing.
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New IXPE X-ray polarimetry and optical monitoring of PG 1553+113 reveal variable polarization and a large EVPA swing, supporting jet models with related but non-co-spatial X-ray and optical emission regions.
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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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Tracking down the broadband polarimetric properties of PG 1553+113
New IXPE X-ray polarimetry and optical monitoring of PG 1553+113 reveal variable polarization and a large EVPA swing, supporting jet models with related but non-co-spatial X-ray and optical emission regions.