Work

A search for periodic active galactic nucleus variability in Gaia Data Release 3

Year: 2026

Type: article

Abstract: Context. Supermassive black hole binaries (SMBHBs) are expected to produce periodic modulations in active galactic nuclei (AGN) light curves, but distinguishing such signals from stochastic red-noise variability remains a major challenge. Aims. We present the first systematic search for statistically significant AGN periodicities using the optical photometry from the Gaia space mission Data Release 3 (DR3), with the goal of identifying SMBHB candidates and establishing a methodological data analysis framework that can be scaled to the forthcoming Data Release 4 (DR4). Methods. We analysed GaiaG -band light curves of 384 152 sources from the Gaia celestial reference frame (CRF3). Stochastic variability was modelled as a damped random walk Gaussian process, and empirical false alarm probabilities were derived by comparing observed Lomb–Scargle periodogram peaks against 100 000 synthetic red-noise realisations. Candidates from this first stage were then re-evaluated using full Markov chain Monte Carlo inference under both exponential and powered-exponential kernels. Results. We find 13 sources satisfying our statistical criterion ( p < α = 10 −5 ) after both stages of filtering, all of which cover fewer than 2.5 cycles of the candidate period and are systematically concentrated in a region of the parameter space indicative of model misspecification. Conclusions. No reliable periodic SMBHB candidates are retained. The ∼950-day baseline of Gaia DR3 confines all detections to the few-cycle regime where red noise most convincingly mimics periodicity, a limitation that photometric precision alone cannot overcome. The longer baseline of Gaia DR4 will be essential to push beyond this regime. We offer our data analysis software pipeline in open access to the community.

Source: Astronomy and Astrophysics

DOI: 10.1051/0004-6361/202660885

Authors: P. Huijse, J. Davelaar, J. De Ridder, N. Jannsen, C. Aerts