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.
Galaxies , year = 2018, month = dec, volume =
2 Pith papers cite this work. Polarity classification is still indexing.
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A review of Faraday Rotation Measure Synthesis techniques and SKA Array Assembly stages for high-resolution Faraday tomography of cosmic magnetic structures.
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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Faraday Tomography with the SKA: A New Era of Cosmic Magnetism Studies
A review of Faraday Rotation Measure Synthesis techniques and SKA Array Assembly stages for high-resolution Faraday tomography of cosmic magnetic structures.