Speaker
Description
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.
