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Abstract The active galactic nucleus (AGN) channel for the formation of binary black hole (BBH) mergers has been previously studied as a potential formation channel for the merging compact binaries observed by the LIGO–Virgo–KAGRA (LVK) scientific collaboration. The first two papers in this series explored theMcFACTScode for the evolution of black hole orbits in AGN accretion disks for individual galaxy models and described the characteristics of predicted BBH populations in realizations of those models (such as the correlation between mass ratio and aligned spin). In this work, we explore the impact of the properties of AGN host galaxies and assume an AGN lifetime and cosmological model for the density of AGN in a universe like our own. By sampling from an inferred population of AGN, we marginalize over galaxy mass to predict a population of BBH mergers observable by modern ground-based gravitational-wave observatories. We find that for reasonable assumptions, AGN disk environments may account for massive BBH mergers such as GW190521 and GW190929_012149. We find that the majority of observable BBH mergers from our simulation are expected to originate in galaxies with a supermassive black hole between 10⁷M_⊙and 10^9.4M_⊙. We also find that if hierarchical mergers from AGN disks account for a substantial part of the LVK population, our current models require an AGN lifetime of 0.5–2.5 Myr. 

Abstract Quasars are bright active galactic nuclei powered by the accretion of matter around supermassive black holes at the center of galaxies. Their stochastic brightness variability depends on the physical properties of the accretion disk and black hole. The upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to observe tens of millions of quasars, so there is a need for efficient techniques like machine learning that can handle the large volume of data. Quasar variability is believed to be driven by an X-ray corona, which is reprocessed by the accretion disk and emitted as UV/optical variability. We are the first to introduce an auto-differentiable simulation of the accretion disk and reprocessing. We use the simulation as a direct component of our neural network to jointly model the driving variability and reprocessing, trained with supervised learning on simulated LSST-like 10 yr quasar light curves. We encode the light curves using a transformer encoder, and the driving variability is reconstructed using latent stochastic differential equations, a physically motivated generative deep learning method that can model continuous-time stochastic dynamics. By embedding the physical processes of the driving signal and reprocessing into our network, we achieve a model that is more robust and interpretable. We demonstrate that our model outperforms a Gaussian process regression baseline and can infer accretion disk parameters and time delays between wave bands, even for out-of-distribution driving signals. Our approach provides a powerful framework that can be adapted to solve other inverse problems in multivariate time series. 

ABSTRACT Tidal disruption events (TDEs) are routinely observed in quiescent galaxies, as stars from the nuclear star cluster are scattered into the loss cone of the central supermassive black hole (SMBH). TDEs are also expected to occur in active galactic nuclei (AGNs), due to scattering or orbital eccentricity pumping of stars embedded in the innermost regions of the AGN accretion disc. Encounters with embedded stellar-mass black holes (BH) can result in AGN μTDEs. AGN TDEs and μTDEs could therefore account for a fraction of observed AGN variability. Here, by performing scattering experiments with the few-body code SpaceHub, we compute the probability of AGN TDEs and μTDEs as a result of 3-body interactions between stars and binary BHs. We find that AGN TDEs are more probable during the early life of the AGNs, when rates are $$\sim (6\times 10^{-5}-5 \times 10^{-2}) (f_\bullet /0.01)\, \rm {AGN}^{-1}$$ yr−1 (where f• is the ratio between the number density of BHs and stars), generally higher than in quiescent galactic nuclei. By contrast, μTDEs should occur throughout the AGN lifetime at a rate of $$\sim (1\times 10^{-4} - 4\times 10^{-2})(f_\bullet /0.01)\, \rm {AGN}^{-1}$$ yr−1. Detection and characterization of AGN TDEs and μAGN TDEs with future surveys using Rubin and Roman will help constrain the populations of stars and compact objects embedded in AGN discs, a key input for the LVK AGN channel. 

Abstract Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar’s brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of AGN in multiple filters over a ten year period, so there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are machine learning models well suited for modeling quasar variability, as they can explicitly capture the underlying stochastic dynamics. In this work, we adapt latent SDEs to jointly reconstruct multivariate quasar light curves and infer their physical properties such as the black hole mass, inclination angle, and temperature slope. Our model is trained on realistic simulations of LSST ten year quasar light curves, and we demonstrate its ability to reconstruct quasar light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multioutput Gaussian process regression baseline. Our method has the potential to provide a deeper understanding of the physical properties of quasars and is applicable to a wide range of other multivariate time series with missing data and irregular sampling. 

ABSTRACT Stars embedded in active galactic nucleus (AGN) discs or captured by them may scatter onto the supermassive black hole (SMBH), leading to a tidal disruption event (TDE). Using the moving-mesh hydrodynamics simulations with arepo, we investigate the dependence of debris properties in in-plane TDEs in AGN discs on the disc density and the orientation of stellar orbits relative to the disc gas (pro- and retro-grade). Key findings are: (1) Debris experiences continuous perturbations from the disc gas, which can result in significant and continuous changes in debris energy and angular momentum compared to ‘naked’ TDEs. (2) Above a critical density of a disc around an SMBH with mass M• [ρcrit ∼ 10−8 g cm−3 (M•/106 M⊙)−2.5] for retrograde stars, both bound and unbound debris is fully mixed into the disc. The density threshold for no bound debris return, inhibiting the accretion component of TDEs, is $$\rho _{\rm crit,bound} \sim 10^{-9}{\rm g~cm^{-3}}(M_{\bullet }/10^{6}\, {\rm M}_{\odot })^{-2.5}$$. (3) Observationally, AGN-TDEs transition from resembling naked TDEs in the limit of ρdisc ≲ 10−2ρcrit,bound to fully muffled TDEs with associated inner disc state changes at ρdisc ≳ ρcrit,bound, with a superposition of AGN + TDE in between. Stellar or remnant passages themselves can significantly perturb the inner disc. This can lead to an immediate X-ray signature and optically detectable inner disc state changes, potentially contributing to the changing-look AGN phenomenon. (4) Debris mixing can enrich the average disc metallicity over time if the star’s metallicity exceeds that of the disc gas. We point out that signatures of AGN-TDEs may be found in large AGN surveys.