  Quasars are bright and unobscured active galactic nuclei (AGN) thought to be
powered by the accretion of matter around supermassive black holes at the
centers of galaxies. The temporal variability of a quasar's brightness contains
valuable information about its physical properties. The UV/optical variability
is thought to be a stochastic process, often represented as a damped random
walk described by a stochastic differential equation (SDE). Upcoming wide-field
telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST)
are expected to observe tens of millions of AGN in multiple filters over a ten
year period, so there is a need for efficient and automated modeling techniques
that can handle the large volume of data. Latent SDEs are machine learning
models well suited for modeling quasar variability, as they can explicitly
capture the underlying stochastic dynamics. In this work, we adapt latent SDEs
to jointly reconstruct multivariate quasar light curves and infer their
physical properties such as the black hole mass, inclination angle, and
temperature slope. Our model is trained on realistic simulations of LSST ten
year quasar light curves, and we demonstrate its ability to reconstruct quasar
light curves even in the presence of long seasonal gaps and irregular sampling
across different bands, outperforming a multioutput Gaussian process regression
baseline. Our method has the potential to provide a deeper understanding of the
physical properties of quasars and is applicable to a wide range of other
multivariate time series with missing data and irregular sampling.
