Quasars with Periodic Variability: Capabilities and Limitations of
  Bayesian Searches for Supermassive Black Hole Binaries in Time-Domain Surveys
  Supermassive black hole binaries (SMBHBs) are an inevitable consequence of
galaxy mergers. At sub-parsec separations, they are practically impossible to
resolve and the most promising technique is to search for quasars with periodic
variability. However, searches for quasar periodicity in time-domain data are
challenging due to the stochastic variability of quasars. In this paper, we
used Bayesian methods to disentangle periodic SMBHB signals from intrinsic
damped random walk (DRW) variability in AGN light curves. We simulated a wide
variety of realistic DRW and DRW+sine light curves. Their observed properties
are modeled after the Catalina Real-time Transient Survey (CRTS) and expected
properties of the upcoming Legacy Survey of Space and Time (LSST) from the Vera
C. Rubin Observatory. Through a careful analysis of parameter estimation and
Bayesian model selection, we investigated the range of parameter space for
which binary systems can be detected. We also examined which DRW signals can
mimic periodicity and be falsely classified as binary candidates. We found that
periodic signals are more easily detectable if the period is short or the
amplitude of the signal is large compared to the contribution of the DRW noise.
We saw similar detection rates both in the CRTS and LSST-like simulations,
while the false detection rate depends on the quality of the data and is
minimal in LSST. Our idealized simulations provide an excellent way to uncover
the intrinsic limitations in quasar periodicity searches and set the stage for
future searches for SMBHBs.
