A Quest for Changing-State AGNs

Abstract

We use Gaia Data Release 3 (DR3) light curves to identify photometrically selected changing-state active galactic nucleus (CSAGN) candidates. We employ unsupervised deep learning and anomaly detection methods to identify unusual patterns of variability in the light curves of 872,228 AGNs with known variability. We find that 8,504 light curves show anomalous behavior. Among these, 682 sources show evidence for two-state photometric behavior during the Gaia DR3 observing baseline. These 682 CSAGN candidates were selected using an automated statistical pattern recognition approach based on Gaussian mixture models (GMMs) to identify sources whose magnitude distributions are consistent with two preferred brightness levels, substantially reducing the need for human visual inspection in selecting the CSAGN candidates. The candidates tend to have fainter absolute magnitudes and lower redshifts than the non-anomalous Gaia variable AGN sample, suggesting that some may be accreting at lower rates. Our approach builds a large candidate sample with less reliance on human visual selection. Follow-up observations of these sources can help identify what drives changing-state transitions and clarify their connection to accretion-disk physics and supermassive black hole (SMBH) fueling.