TrackFormers - Machine Learning Pipelines
[Contributors: Sascha Caron, José D. Martin-Guerrero, Uraz Odyurt, Roberto Ruiz de Austri Bazan]
Abstract
TrackFormers is a machine learning framework for track reconstruction in particle
physics experiments. It leverages transformer- and U-Net-inspired deep learning
architectures to predict particle tracks from hit data.
This repository contains 4 directories corresponding to the 4 models described in the
paper TrackFormers: In Search of Transformer-Based Particle Tracking for the
High-Luminosity LHC Era. EncDec, EncCla, and EncReg are transformer-based models,
whereas U-Net is, as the name suggests, a U-Net model.
Refer to the provided README file for further details.
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