Deep modeling of quasar variability
  Quasars have long been known as intrinsically variable sources, but the
physical mechanism underlying the temporal optical/UV variability is still not
well understood. We propose a novel nonparametric method for modeling and
forecasting the optical variability of quasars utilizing an autoencoder neural
network to gain insight into the underlying processes. The autoencoder is
trained with ~15,000 decade-long quasar light curves obtained by the Catalina
Real-time Transient Survey selected with negligible flux contamination from the
host galaxy. The autoencoder's performance in forecasting the temporal flux
variation of quasars is superior to that of the damped random walk process. We
find a temporal asymmetry in the optical variability and a novel relation - the
amplitude of the variability asymmetry decreases as luminosity and/or black
hole mass increases - is suggested with the help of autoencoded features. The
characteristics of the variability asymmetry are in agreement with those from
the self-organized disk instability model, which predicts that the magnitude of
the variability asymmetry decreases as the ratio of the diffusion mass to
inflow mass in the accretion disk increases.
