  The survey of the COSMOS field by the VLT Survey Telescope is an appealing
testing ground for variability studies of active galactic nuclei (AGN). With 54
r-band visits over 3.3 yr and a single-visit depth of 24.6 r-band mag, the
dataset is also particularly interesting in the context of performance
forecasting for the Vera C. Rubin Observatory Legacy Survey of Space and Time
(LSST). This work is the fifth in a series dedicated to the development of an
automated, robust, and efficient methodology to identify optically variable
AGN, aimed at deploying it on future LSST data. We test the performance of a
random forest (RF) algorithm in selecting optically variable AGN candidates,
investigating how the use of different AGN labeled sets (LSs) and features sets
affects this performance. We define a heterogeneous AGN LS and choose a set of
variability features and optical and near-infrared colors based on what can be
extracted from LSST data. We find that an AGN LS that includes only Type I
sources allows for the selection of a highly pure (91%) sample of AGN
candidates, obtaining a completeness with respect to spectroscopically
confirmed AGN of 69% (vs. 59% in our previous work). The addition of colors to
variability features mildly improves the performance of the RF classifier,
while colors alone prove less effective than variability in selecting AGN as
they return contaminated samples of candidates and fail to identify most
host-dominated AGN. We observe that a bright (r < 21 mag) AGN LS is able to
retrieve candidate samples not affected by the magnitude cut, which is of great
importance as faint AGN LSs for LSST-related studies will be hard to find and
likely imbalanced. We estimate a sky density of 6.2 million AGN for the LSST
main survey down to our current magnitude limit.
