  Track finding in particle data is a challenging pattern recognition problem
in High Energy Physics. It takes as inputs a point cloud of space points and
labels them so that space points created by the same particle have the same
label. The list of space points with the same label is a track candidate. We
argue that this pattern recognition problem can be formulated as a sorting
problem, of which the inputs are a list of space points sorted by their
distances away from the collision points and the outputs are the space points
sorted by their labels. In this paper, we propose the TrackSorter algorithm: a
Transformer-based algorithm for pattern recognition in particle data.
TrackSorter uses a simple tokenization scheme to convert space points into
discrete tokens. It then uses the tokenized space points as inputs and sorts
the input tokens into track candidates. TrackSorter is a novel end-to-end track
finding algorithm that leverages Transformer-based models to solve pattern
recognition problems. It is evaluated on the TrackML dataset and has good track
finding performance.
