  In this work, we present a study on ways that tracking algorithms can be
improved with machine learning (ML). We base this study on the line segment
tracking (LST) algorithm that we have designed to be naturally parallelized and
vectorized in order to efficiently run on modern processors. LST has been
developed specifically for the CMS Experiment at the LHC, towards the High
Luminosity LHC (HL-LHC) upgrade. Moreover, we have already shown excellent
efficiency and performance results as we iteratively improve LST, leveraging a
full simulation of the CMS detector. At the same time, promising
deep-learning-based tracking algorithms, such as Graph Neural Networks (GNNs),
are being pioneered on the simplified TrackML dataset. These results suggest
that parts of LST could be improved or replaced by ML. Thus, a thorough,
step-by-step investigation of exactly how and where ML can be utilized, while
still meeting realistic HL-LHC performance and efficiency constraints, is
implemented as follows. First, a lightweight neural network is used to replace
and improve upon explicitly defined track quality selections. This neural
network is shown to be highly efficient and robust to displaced tracks while
having little-to-no impact on the runtime of LST. These results clearly
establish that ML can be used to improve LST without penalty. Next, exploratory
studies of GNN track-building algorithms are described. In particular,
low-level track objects from LST are considered as nodes in a graph, where
edges represent higher-level objects or even entire track candidates. Then, an
edge-classifier GNN is trained, and the efficiency of the resultant edge scores
is compared with that of the existing LST track quality selections. These GNN
studies provide insights into the practicality and performance of using more
ambitious and complex ML algorithms for HL-LHC tracking at the CMS Experiment.
