Distinguishing Black Hole Jet States via Visibility Data

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This work explores the magnetic mechanisms influencing black hole jet states, distinguishing MAD and SANE states based on stability and variability. It highlights observational signatures, integrates GRMHD simulations with data, utilizes machine learning, and proposes innovative visibility-domain techniques to match jet states. The study connects theoretical frameworks to observable phenomena, advancing black hole physics and shaping future observational strategies.