Particle tracking is a challenging pattern recognition task at the Large Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms, such as those based on the Kalman Filter, achieve excellent performance in reconstructing the prompt tracks from the collision points. However, they require dedicated configuration and additional computing time to efficiently reconstruct the large radius tracks created away from the collision points. We developed an end-to-end machine learning-based track finding algorithm for the HL-LHC, the Exa.TrkX pipeline. The pipeline is designed so as to be agnostic about global track positions. In this work, we study the performance of the Exa.TrkX pipeline for finding large radius tracks. Trained with all tracks in the event, the pipeline simultaneously reconstructs prompt tracks and large radius tracks with high efficiencies. This new capability offered by the Exa.TrkX pipeline may enable us to search for new physics in real time.
@article{arxiv.2203.08800,
title = {Reconstruction of Large Radius Tracks with the Exa.TrkX pipeline},
author = {Chun-Yi Wang and Xiangyang Ju and Shih-Chieh Hsu and Daniel Murnane and Paolo Calafiura and Steven Farrell and Maria Spiropulu and Jean-Roch Vlimant and Adam Aurisano and V Hewes and Giuseppe Cerati and Lindsey Gray and Thomas Klijnsma and Jim Kowalkowski and Markus Atkinson and Mark Neubauer and Gage DeZoort and Savannah Thais and Alexandra Ballow and Alina Lazar and Sylvain Caillou and Charline Rougier and Jan Stark and Alexis Vallier and Jad Sardain},
journal= {arXiv preprint arXiv:2203.08800},
year = {2023}
}
Comments
5 pages, 3 figures. Proceedings of 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research