Accretion disks around supermassive black holes in active galactic nuclei produce continuum radiation at ultraviolet and optical wavelengths. Physical processes in the accretion flow lead to stochastic variability of this emission on a wide range of time scales. We measured the optical continuum variability observed in 67 active galactic nuclei and the characteristic time scale at which the variability power spectrum flattens. We found a correlation between this time scale and the black hole mass extending over the entire mass range of supermassive black holes. This time scale is consistent with the expected thermal time scale at the ultraviolet-emitting radius in standard accretion disk theory. Accreting white dwarfs lie close to this correlation, suggesting a common process for all accretion disks.

The recent report of an association of the gravitational-wave (GW) binary black hole (BBH) merger GW190521 with a flare in the active galactic nuclei (AGNs) J124942.3 + 344929 has generated tremendous excitement. However, GW190521 has one of the largest localization volumes among all of the GW events detected so far. The 90% localization volume likely contains 7400 unobscured AGNs brighter than g ≤ 20.5 AB mag, and it results in a ≳70% probability of chance coincidence for an AGN flare consistent with the GW event. We present a Bayesian formalism to estimate the confidence of an AGN association by analyzing a population of BBH events with dedicated follow-up observations. Depending on the fraction of BBHs arising from AGNs, counterpart searches of  ( 1 ) −  ( 100 ) GW events are needed to establish a confident association, and more than an order of magnitude more for searches without follow-up (i.e., using only the locations of AGN and GW events). Follow-up campaigns of the top ∼5% (based on volume localization and binary mass) of BBH events with total rest-frame mass ≥50 M ⊙ are expected to establish a confident association during the next LIGO/Virgo/KAGRA observing run (O4), as long as the true value of the fraction of BBHs giving rise to AGN flares is >0.1. Our formalism allows us to jointly infer cosmological parameters from a sample of BBH events that include chance coincidence flares. Until the confidence of AGN associations is established, the probability of chance coincidence must be taken into account to avoid biasing astrophysical and cosmological constraints.

We apply a new deep learning technique to detect, classify, and deblend sources in multi-band astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask R-CNN image processing framework, a general code for efficient object detection, classification, and instance segmentation. After evaluating the performance of our network against simulated ground truth images for star and galaxy classes, we find a precision of 92% at 80% recall for stars and a precision of 98% at 80% recall for galaxies in a typical field with ∼ 30 galaxies/arcmin 2 . We investigate the deblending capability of our code, and find that clean deblends are handled robustly during object masking, even for significantly blended sources. This technique, or extensions using similar network architectures, may be applied to current and future deep imaging surveys such as LSST and WFIRST. Our code, Astro R-CNN, is publicly available at https://github.com/burke86/astro_rcnn.