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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