FIRE-2 simulations show per-galaxy tidal disruption rates peak near z=2.5 at 4e-4 per year, correlate with SFR and central density, and remain high in satellite galaxies at early times.
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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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TDEs on FIRE: Illuminating the Cosmic Evolution of Tidal Disruption Rates
FIRE-2 simulations show per-galaxy tidal disruption rates peak near z=2.5 at 4e-4 per year, correlate with SFR and central density, and remain high in satellite galaxies at early times.
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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.