TrackFormers - Machine Learning Pipelines
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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