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Scalable and Robust Multiband Modeling of AGN Light Curves in Rubin-LSST
Journal article   Open access   Peer reviewed

Scalable and Robust Multiband Modeling of AGN Light Curves in Rubin-LSST

Weixiang Yu, John J. Ruan, Colin J. Burke, Roberto J. Assef, Tonima T. Ananna, Franz E. Bauer, Demetra De Cicco, Keith Horne, Lorena Hernandez-Garcia, Dragana Ilic, …
The Astrophysical journal, v 998(1), 144
10 Feb 2026
DOI: https://doi.org/10.3847/1538-4357/ae28d3
url
https://doi.org/10.3847/1538-4357/ae28d3View
Published, Version of Record (VoR) Open

Abstract

Astronomy & Astrophysics Science & Technology Physical Sciences
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will monitor tens of millions of active galactic nuclei (AGNs) for a period of 10 yr with an average cadence of 3 days in six broad photometric bands. This unprecedented data set will enable robust characterizations of AGN UV/optical variability across a wide range of AGN physical properties. However, existing tools for modeling AGN light curves are not yet capable of fully leveraging the volume, cadence, and multiband nature of LSST data. We present EzTaoX, a scalable light-curve modeling tool designed to take advantage of LSST's multiband observations to simultaneously characterize AGN UV/optical stochastic variability and measure interband time delays. EzTaoX achieves a speed increase of similar to 102-104x on CPUs over current tools with similar capabilities, while maintaining equal or better accuracy in recovering simulated variability properties. This performance gain enables continuum time-delay measurements for all AGNs discovered by LSST-both in the Wide Fast Deep survey and the Deep Drilling Fields-thereby opening new opportunities to probe AGN accretion-flow geometries. In addition, EzTaoX's multiband capability allows for robust characterization of AGN stochastic variability down to hourly timescales, facilitating the identification of accreting low-mass AGNs-such as those residing in dwarf galaxies-through their distinctive variability signatures.

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