Identifying massive black hole binaries via light curve variability in optical time-domain surveys

Abstract

Accreting massive black hole binaries (MBHBs) often display periodic variations in their emitted radiation, providing a distinctive signature for their identification. In this work, we explore the MBHBs identification via optical variability studies by simulating the observations of the LSST survey. To this end, we generate a population of MBHBs using the L-Galaxies semi-analytical model, focusing on systems with observed orbital periods $\le$ 5 years. This ensures that at least two complete cycles of emission can be observed within the 10-year mission of LSST. To construct mock optical light curves, we first calculate the MBHB average magnitudes in each LSST filter by constructing a self-consistent SED that accounts for the binary accretion history and the emission from a circumbinary disc and mini-discs. We then add variability modulations by using six 3D hydrodynamic simulations of accreting MBHBs with different eccentricities and mass ratios as templates. To make the light curves realistic, we mimic the LSST observation patterns and cadence, and we include stochastic variability and LSST photometric errors. Our results show from $10^{-2}$ to $10^{-1}$ MBHBs per square degree, with light curves that are potentially detectable by LSST. These systems are mainly low-redshift ($z\lesssim 1.5$), massive ($\gtrsim 10^7{M}_{\odot }$), equal-mass ($\sim 0.8$), relatively eccentric ($\sim 0.6$), and with modulation periods of around $3.5$ years. Using periodogram analysis, we find that LSST variability studies have a higher success rate ($>$50%) for systems with high eccentricities ($e>$0.6). Additionally, at fixed eccentricity, detections tend to favour systems with more unequal mass ratios. The false alarm probability shows similar trends. Circular binaries systematically feature high values ($\gtrsim 10^{-1}$). Eccentric systems have low-FAP tails, down to $\sim 10^{-8}$.

Submitted to A&A, 13 pages, 8 figures, 2 tables

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