Dec 11 – 15, 2023
Tsung-Dao Lee Institute
Asia/Shanghai timezone

Identifying the electromagnetic counterparts of LISA's massive black hole binaries in archival LSST data

Dec 15, 2023, 2:45 PM
Poster Poster


Chengcheng Xin (Columbia University)


Xin & Haiman (2021) predicted that the Vera C Rubin Observatory’s Legacy Survey of Space and Time (LSST) will observe up to 100 million quasars. Among these up to approximately 100 ultra-compact massive black hole binaries binaries can be identified, which 5-15 years later can then be detected in gravitational waves (GWs) by the future Laser Interferometer Space Antenna (LISA). In this talk, I will present the reverse analysis: given that GWs from a massive black hole binary have been detected by LISA, we assess whether or not a unique electromagnetic (EM) counterpart for the same binary can be identifed in archival LSST data as a periodically varying AGN. We use the binary properties derived from the LISA waveform, such as the evolution of orbital frequency, the total mass, distance and the sky localization, to predict the redshift, magnitude and historical periodicity of the AGN expected in the archival LSST data. We then use Monte Carlo simulations to compute the false alarm rate, i.e. the number of AGN matching these properties by chance, based on the (extrapolated) quasar luminosity function, the sampling cadence of LSST, and the intrinsic "damped random walk (DRW)" quasar variability. We perform our analysis on four fiducial LISA binaries, with masses and redshifts of ($M_{\rm BH}/M_{\odot}$, z) = ($3\times10^5$, 0.3), ($3\times10^6$, 0.3), ($10^7$, 0.3) and ($10^7$, 1). We conclude that (i) it may be possible to identify the unique LSST archival source for each of the four fiducial LISA binaries, (ii) the least massive BH binary, ($M_{\rm BH}/M_{\odot}$, z) = ($3\times10^5$, 0.3), has the highest false alarm rate of ~7%-55%, and (iii) the other three binaries yield excellent chances to be uniquely identified in LSST, with false alarm rates below $10^{-6}$.

Primary author

Chengcheng Xin (Columbia University)


Zoltan Haiman (Columbia University)

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