Transformers for Particle Track Reconstruction and Hit Clustering
Abstract
Track reconstruction is a crucial part of High Energy Physics (HEP) experiments.
Traditional methods for the task scale poorly, making machine learning and deep
learning appealing alternatives. Following the success of transformers in the field
of language processing, we investigate the feasibility of training a Transformer to
translate detector signals into track parameters. We study and compare different
architectures. Firstly, an autoregressive Transformer model with the original
encoder-decoder architecture which reconstructs a particle's trajectory given a few
initial hits. Secondly, an encoder-only architecture used as a classifier, producing
a class label for each hit in an event, given pre-defined bins within the track
parameter space. Lastly, an encoder-only model with the purpose of regressing track
parameter values for each hit in an event, followed by clustering.
The Transformer models are benchmarked on simplified datasets generated by the
recently developed simulation framework REDuced VIrtual Detector (REDVID) as well as
a subset of the TrackML data. The preliminary results of the proposed models show
promise for the application of these deep learning techniques on more realistic data
for particle reconstruction.
This work has been previously presented at the following conferences: Connecting The
Dots 2023 (https://indico.cern.ch/event/1252748/contributions/5521505/), NNV 2023
(https://indico.nikhef.nl/event/4510/contributions/18909/), and ML4Jets2023
(https://indico.cern.ch/event/1253794/contributions/5588602/).
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