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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4 Pith papers cite this work. Polarity classification is still indexing.
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GRMHD models show PA1 aligns with the approaching limb for high spins, enabling mild disfavoring of low spins and strong disfavoring of Earth-pointing spins in M87* from EHT data, with similar potential for Sgr A*.
LILA can detect IMBH binaries at redshifts 20-30, IMRIs, and provide months-to-years early warnings with high-SNR events for gravity tests.
GPU version of OpenGadget3 matches CPU results across multiple test suites with chip-to-chip speedups of 2-5x.
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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Ring Position Angles and Spin in M87* and Sgr A*
GRMHD models show PA1 aligns with the approaching limb for high spins, enabling mild disfavoring of low spins and strong disfavoring of Earth-pointing spins in M87* from EHT data, with similar potential for Sgr A*.
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Black Hole Binary Detection Landscape for the Laser Interferometer Lunar Antenna (LILA): Signal-to-Noise Calculations & Science Cases
LILA can detect IMBH binaries at redshifts 20-30, IMRIs, and provide months-to-years early warnings with high-SNR events for gravity tests.