Towards Novel Charged Particle Tracking Approaches with Transformer and U-Net Models
Abstract
Inspired by the recent successes of language modelling and computer vision machine learning
techniques, we study the feasibility of repurposing these developments for particle track
reconstruction in the context of high energy physics. In particular, drawing from developments in
the field of language modelling we showcase the performance of multiple implementations of the
transformer model, including an autoregressive transformer with the original encoder-decoder
architecture, and encoder-only architectures for the purpose of track parameter classification and
clustering. Furthermore, in the context of computer vision we study a U-net style model with
submanifold convolutions, treating the event as an image and highlighting those pixels where a hit
was detected.
We benchmark these models on simplified training data utilising a recently developed simulation
framework, REDuced VIrtual Detector (REDVID). These data include noisy linear and helical track
definitions, similar to those observed in particle detectors from major LHC collaborations such as
ATLAS and CMS. We find that the proposed models can be used to effectively reconstruct particle
tracks on this simplified dataset, and we compare their performances both in terms of reconstruction
efficiency and runtime. As such, this work lays the necessary groundwork for developments in the
near future towards such novel machine learning strategies for particle tracking on more realistic
data.
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