Relativistic Gas Accretion onto Supermassive Black Hole Binaries from Inspiral through Merger (2502.06389v3)
Abstract: Accreting supermassive black hole binaries are powerful multimessenger sources emitting both gravitational and EM radiation. Understanding the accretion dynamics of these systems and predicting their distinctive EM signals is crucial to informing and guiding upcoming efforts aimed at detecting gravitational waves produced by these binaries. To this end, accurate numerical modeling is required to describe both the spacetime and the magnetized gas around the black holes. In this paper, we present two key advances in this field of research. First, we have developed a novel 3D GRMHD framework that combines multiple numerical codes to simulate the inspiral and merger of supermassive black hole binaries starting from realistic initial data and running all the way through merger. Throughout the evolution, we adopt a simple but functional prescription to account for gas cooling through photon emission. Next, we have applied our new computational method to follow the time evolution of a circular, equal-mass, non-spinning black hole binary for ~200 orbits, starting from a separation of 20r_g and reaching the post-merger evolutionary stage of the system. We have shown how mass continues to flow toward the binary even after the binary "decouples" from its surrounding disk, but the accretion rate onto the black holes diminishes. We have identified how the minidisks orbiting each black hole are slowly drained and eventually dissolve as the binary compresses. We confirm previous findings that the system's luminosity decreases by only a factor of a few during inspiral; however, we observe an abrupt increase by ~50% in this quantity at the time of merger, likely accompanied by an equally abrupt change in spectrum. Finally, we have demonstrated that during the inspiral, fluid ram pressure regulates the fraction of the magnetic flux transported to the binary that attaches to the black holes' horizons.
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.