TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Era
Abstract
High-Energy Physics experiments are facing a multi-fold data increase with every new iteration. This
is certainly the case for the upcoming High-Luminosity LHC upgrade. Such increased data processing
requirements forces revisions to almost every step of the data processing pipeline. One such step in
need of an overhaul is the task of particle track reconstruction, a.k.a., tracking. A Machine
Learning-assisted solution is expected to provide significant improvements, since the most
time-consuming step in tracking is the assignment of hits to particles or track candidates. This is
the topic of this paper.
We take inspiration from large language models. As such, we consider two approaches: the prediction
of the next word in a sentence (next hit point in a track), as well as the one-shot prediction of
all hits within an event. In an extensive design effort, we have experimented with three models
based on the Transformer architecture and one model based on the U-Net architecture, performing
track association predictions for collision event hit points. In our evaluation, we consider a
spectrum of simple to complex representations of the problem, eliminating designs with lower metrics
early on. We report extensive results, covering both prediction accuracy (score) and computational
performance. We have made use of the REDVID simulation framework, as well as reductions applied to
the TrackML data set, to compose five data sets from simple to complex, for our experiments. The
results highlight distinct advantages among different designs in terms of prediction accuracy and
computational performance, demonstrating the efficiency of our methodology. Most importantly, the
results show the viability of a one-shot encoder-classifier based Transformer solution as a
practical approach for the task of tracking.
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