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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