Conditional Neural Process for non-parametric modeling of AGN light
  curve
  The consequences of complex disturbed environments in the vicinity of a
supermassive black hole are not well represented by standard statistical models
of optical variability in active galactic nuclei (AGN). Thus, developing new
methodologies for investigating and modeling AGN light curves is crucial.
Conditional Neural Processes (CNPs) are nonlinear function models that forecast
stochastic time-series based on a finite amount of known data without the use
of any additional parameters or prior knowledge (kernels). We provide a CNP
algorithm that is specifically designed for simulating AGN light curves. It was
trained using data from the All-Sky Automated Survey for Supernovae, which
included 153 AGN. We present CNP modeling performance for a subsample of five
AGNs with distinctive difficult-to-model properties. The performance of CNP in
predicting temporal flux fluctuation was assessed using a minimizing loss
function, and the results demonstrated the algorithm's usefulness. Our
preliminary parallelization experiments show that CNP can efficiently handle
large amounts of data. These results imply that CNP can be more effective than
standard tools in modeling large volumes of AGN data (as anticipated from
time-domain surveys such as the Vera C. Rubin Observatory's Legacy Survey of
Space and Time).
