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Supermassive black hole binaries (SMBHBs) are an inevitable consequence ofgalaxy mergers. At subparsec separations, they are practically impossible toresolve, and the most promising technique is to search for quasars withperiodic variability. However, searches for quasar periodicity in time-domaindata are challenging due to the stochastic variability of quasars. In thispaper, we used Bayesian methods to disentangle periodic SMBHB signals fromintrinsic damped random walk (DRW) variability in active galactic nuclei lightcurves. We simulated a wide variety of realistic DRW and DRW+sine light curves.Their observed properties are modeled after the Catalina Real-time TransientSurvey (CRTS) and expected properties of the upcoming Legacy Survey of Spaceand Time (LSST) from the Vera C. Rubin Observatory. Through a careful analysisof parameter estimation and Bayesian model selection, we investigated the rangeof parameter space for which binary systems can be detected. We also examinedwhich DRW signals can mimic periodicity and be falsely classified as binarycandidates. We found that periodic signals are more easily detectable if theperiod is short or the amplitude of the signal is large compared to thecontribution of the DRW noise. We saw similar detection rates both in the CRTSand LSST-like simulations, while the false-detection rate depends on thequality of the data and is minimal in LSST. Our idealized simulations providean excellent way to uncover the intrinsic limitations in quasar periodicitysearches and set the stage for future searches for SMBHBs.

Quasars with Periodic Variability: Capabilities and Limitations of Bayesian Searches for Supermassive Black Hole Binaries in Time-Domain Surveys