Optimizing Optical Searches for Supermassive Black Hole Binaries in AGN Light Curves: Fourier versus Bayesian Periodicity Detection

Abstract: Simulations predict that supermassive black hole binaries (SMBHBs) will exhibit periodic brightness variations that may exceed the stochastic variability intrinsic to active galactic nuclei (AGN). In this paper, we simulate SMBHBs with damped random walk (DRW) AGN variability and an added sinusoidal signal from the orbital motion, and test three methods -- the Generalized Lomb Scargle Periodogram (GLSP), the nested Bayesian sampler (NBS), and the Weighted Wavelet Z-Transform (WWZ) -- to determine which is best at recovering the periodicity. Our simulated light curves follow the properties of the Catalina Real-Time Transient Survey (CRTS), Legacy Survey of Space and Time (LSST), and Zwicky Transient Facility (ZTF) to best inform current and future SMBHB searches. We map a broad range of parameter space and identify which DRW-only light curves best mimic periodicity and pass each method's model selection. The NBS performs best at detecting periodicity and filtering out DRW-only light curves. Combined candidate selection with both the NBS and GLSP significantly reduces false positive rates with marginal impact to true positive rates. With this joint model selection pipeline, we find the lowest false positive rates in ZTF-like simulations and the highest detection rates in LSST-like simulations. Using a modified computation of the False Alarm Probability (FAP) with GLSP, we efficiently triage LSST AGN light curves (~10^7 light curves in ~10-30 hours) and achieve true- and false- positive rates of ~40% and ~0.5%, respectively.