  The locations of proton-proton collision points in LHC experiments are called
primary vertices (PVs). Preliminary results of a hybrid deep learning algorithm
for identifying and locating these, targeting the Run 3 incarnation of LHCb,
have been described at conferences in 2019 and 2020. In the past year we have
made significant progress in a variety of related areas. Using two newer Kernel
Density Estimators (KDEs) as input feature sets improves the fidelity of the
models, as does using full LHCb simulation rather than the "toy Monte Carlo"
originally (and still) used to develop models. We have also built a deep
learning model to calculate the KDEs from track information. Connecting a
tracks-to-KDE model to a KDE-to-hists model used to find PVs provides a
proof-of-concept that a single deep learning model can use track information to
find PVs with high efficiency and high fidelity. We have studied a variety of
models systematically to understand how variations in their architectures
affect performance. While the studies reported here are specific to the LHCb
geometry and operating conditions, the results suggest that the same approach
could be used by the ATLAS and CMS experiments.
