TrackFormers - Machine Learning Pipelines

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.