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Aligning Nuclear Cluster Orbits with an Active Galactic Nucleus Accretion Disk

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arxiv 2006.11229 v2 pith:UMWED56D submitted 2020-06-19 astro-ph.GA astro-ph.HE

classification astro-ph.GAastro-ph.HE
keywords diskstellardragembeddedaccretionactiveblackcapture
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Active galactic nuclei (AGN) are powered by the accretion of disks of gas onto supermassive black holes (SMBHs). Stars and stellar remnants orbiting the SMBH in the nuclear star cluster (NSC) will interact with the AGN disk. Orbiters plunging through the disk experience a drag force and, through repeated passage, can have their orbits captured by the disk. A population of embedded objects in AGN disks may be a significant source of binary black hole mergers, supernovae, tidal disruption events and embedded gamma-ray bursts. For two representative AGN disk models we use geometric drag and Bondi-Hoyle-Littleton drag to determine the time to capture for stars and stellar remnants. We assume a range of initial inclination angles and semi-major axes for circular Keplerian prograde orbiters. Capture time strongly depends on the density and aspect ratio of the chosen disk model, the relative velocity of the stellar object with respect to the disk, and the AGN lifetime. We expect that for an AGN disk density $\rho \gtrsim 10^{-11}\rm g/cm^3$ and disk lifetime $\geq 1$Myr, there is a significant population of embedded stellar objects, which can fuel mergers detectable in gravitational waves with LIGO-Virgo and LISA.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Dissecting environmental effects with eccentric gravitational wave sources

    astro-ph.HE 2025-06 conditional novelty 7.0 of 10

    Resonances between oscillating environmental forces and the epicyclic motion of eccentric binaries can dominate gravitational wave dephasing over orbit-averaged drag for eccentricities above about 0.05.

  2. Improving gravitational wave search sensitivity with TIER: Trigger Inference using Extended strain Representation

    gr-qc 2025-07 conditional novelty 6.0 of 10

    A machine learning classifier trained on the extended noise environment around gravitational wave candidates improves search sensitivity for heavy, unequal-mass black hole mergers by up to roughly 20 percent.

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