Identifying compact chirping SMBHBs in LSST using Bayesian analysis

Xin C, Isi M, Farr WM, Haiman Z. 2026. Identifying compact chirping SMBHBs in LSST using Bayesian analysis. The Astrophysical Journal. 999(1), 149.

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Author
Xin, Chengcheng; Isi, Maximiliano; Farr, Will M.; Haiman, ZoltánISTA
Department
Abstract
The Legacy Survey of Space and Time (LSST) is expected to observe up to ∼100 million quasars in the next decade. In this work, we show that it is possible to use such data to measure the characteristic frequency evolution of a “chirp” induced by gravitational waves, which can serve as robust evidence for the presence of a compact supermassive black hole binary. Following the LSST specifications, we generate mock lightcurves consisting of (i) a post-Newtonian chirp produced by orbital motion through, e.g., relativistic Doppler boosting, (ii) a damped random walk representing intrinsic quasar variability, and (iii) Gaussian photometric errors, while assuming nonuniform observations with extended gaps over a period of 10 yr. Through a fully Bayesian analysis, we show that we can simultaneously measure the chirp and noise parameters with little degeneracy between the two. For chirp signals with an amplitude of A = 0.5 mag and a range of times to merger (tm = 15–104 yr), we can typically measure a nonzero amplitude and positive frequency derivative with over 5σ credibility. For binaries with tm = 50 yr, we achieve 3σ (5σ) confidence that the signal is chirping for A ≳ 0.1 (A > 0.2). The median runtime of our analysis is 5.6 minutes, with a minimum as low as 35 s, making it scalable to a large number of lightcurves. This implies that LSST could, on its own, establish the presence of a compact supermassive black hole binary, and thus discover gravitational wave sources detectable by LISA and by Pulsar Timing Arrays.
Publishing Year
Date Published
2026-02-27
Journal Title
The Astrophysical Journal
Publisher
IOP Publishing
Acknowledgement
C.X. thanks the Department of Astronomy of Columbia University, where the majority of this work was performed. Z.H. thanks the Center for Computational Astrophysics (CCA) at the Flatiron Institute for their hospitality during a sabbatical stay, where this work was initiated. The Flatiron Institute is a division of the Simons Foundation. This work was supported in part by NASA’s LISA Preparatory Science Program (LPS) through grant 80NSSC24K0440 and by NASA Astrophysics Theory Program (ATP) grant 80NSSC22K0822. The authors thank the anonymous referee for helpful feedback, which improved this manuscript. We also thank Prof. Brian D. Metzger, Prof. David Schiminovich, and Prof. Kishalay De for valuable discussions. Software: numpyro (E. Bingham et al. 2019; D. Phan et al. 2019); celerite2 (D. Foreman-Mackey et al. 2017; D. Foreman-Mackey 2018); Python (G. Van Rossum & F. L. Drake 2009); The Software Citation Station (T. Wagg & F. S. Broekgaarden 2024; T. Wagg et al. 2024).
Volume
999
Issue
1
Article Number
149
ISSN
eISSN
IST-REx-ID

Cite this

Xin C, Isi M, Farr WM, Haiman Z. Identifying compact chirping SMBHBs in LSST using Bayesian analysis. The Astrophysical Journal. 2026;999(1). doi:10.3847/1538-4357/ae40b3
Xin, C., Isi, M., Farr, W. M., & Haiman, Z. (2026). Identifying compact chirping SMBHBs in LSST using Bayesian analysis. The Astrophysical Journal. IOP Publishing. https://doi.org/10.3847/1538-4357/ae40b3
Xin, Chengcheng, Maximiliano Isi, Will M. Farr, and Zoltán Haiman. “Identifying Compact Chirping SMBHBs in LSST Using Bayesian Analysis.” The Astrophysical Journal. IOP Publishing, 2026. https://doi.org/10.3847/1538-4357/ae40b3.
C. Xin, M. Isi, W. M. Farr, and Z. Haiman, “Identifying compact chirping SMBHBs in LSST using Bayesian analysis,” The Astrophysical Journal, vol. 999, no. 1. IOP Publishing, 2026.
Xin C, Isi M, Farr WM, Haiman Z. 2026. Identifying compact chirping SMBHBs in LSST using Bayesian analysis. The Astrophysical Journal. 999(1), 149.
Xin, Chengcheng, et al. “Identifying Compact Chirping SMBHBs in LSST Using Bayesian Analysis.” The Astrophysical Journal, vol. 999, no. 1, 149, IOP Publishing, 2026, doi:10.3847/1538-4357/ae40b3.
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2026-10-14
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