Transformer-Inspired Models for Particle Track Reconstruction
Abstract
Particle track reconstruction is a fundamental aspect of experimental analysis in
high-energy particle physics. Conventional methodologies for track reconstruction are
suboptimal in terms of efficiency in anticipation of the High Luminosity phase of the
Large Hadron Collider. This has motivated researchers to explore the latest developments
in deep learning for their scalability and potential enhanced inference efficiency.
We assess the feasibility of three Transformer-inspired model architectures for hit
clustering and classification. The first model uses an encoder-decoder architecture to
reconstruct a track auto-regressively, given the coordinates of the first few hits. The
second model employs an encoder-only architecture as a classifier, using predefined
labels for each track. The third model, also utilising an encoder-only configuration,
regresses track parameters, and subsequently assigns clusters in the track parameter
space to individual tracks.
We discuss preliminary studies on a simplified dataset, showing high success rates for
all models under consideration, alongside our latest results using the TrackML dataset
from the 2018 Kaggle challenge. Additionally, we present our journey in the adaptation of
models and training strategies, addressing the trade-offs among training efficiency,
accuracy, and the optimisation of sequence lengths within the memory constraints of the
hardware at our disposal.
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