TrackSorter: A Transformer-based sorting algorithm for track finding in High Energy Physics
This addresses pattern recognition for particle data analysis in high-energy physics, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the track finding problem in high-energy physics by formulating it as a sorting task and proposes TrackSorter, a Transformer-based algorithm that achieves good performance on the TrackML dataset.
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.