A population study for searching supermassive binary black holes in active galactic nuclei: continuum spectral features and periodic variabilities
Zekun Li, Changshuo Yan, Youjun LuABSTRACT
Active sub-pc supermassive binary black holes (SMBBHs) are expected to exhibit various electromagnetic signatures due to unique dynamical and geometric structures of their accretion, but observational identification of them remains challenging. In this paper, we adopt semianalytic models to investigate both deficits in spectral energy distributions (SEDs) of these systems induced by gaps/holes in their accretion discs and periodic variations in their light curves induced by either orbital-modulated Doppler boosting or accretion rate variation of each SMBH component. We construct a population model to generate SMBBHs across cosmic time by considering their orbital evolution and associated accretion and radiation processes. By estimating the continuum emission from each mock system and its variation, we investigate the detection of SMBBHs via either SED-deficit signature or light-curve periodicity under reasonably given criteria. We find that all-sky surveys with filters similar to those of the China Space Station Telescope or Rubin/LSST could identify up to approximately $3\times 10^2$ SMBBHs via SED-deficit features and/or $10^3$ SMBBHs via periodicity ($\lesssim 5$ yr), although both selections may suffer from a high rate of false positives. A few to a dozen SED-deficit SMBBHs may be detected by future pulsar timing arrays with signal-to-noise ratio $\gtrsim 3$, enabling multimessenger observations. Only $\sim 20~{{\ \rm per\ cent}}-53~{{\ \rm per\ cent}}$ of SED-deficit selected SMBBHs may also be detected via periodic variations, and $\sim 7~{{\ \rm per\ cent}}-9~{{\ \rm per\ cent}}$ of periodic variation selected SMBBHs may be detected via SED-deficit signatures. The false positives for those SMBBHs selected jointly by both methods are negligible, highlighting the importance of searching for active SMBBH systems using joint methods.