TY - JOUR
T1 - Reconstruction of Large Radius Tracks with the Exa.TrkX pipeline
AU - Wang, Chun Yi
AU - Ju, Xiangyang
AU - Hsu, Shih Chieh
AU - Murnane, Daniel
AU - Calafiura, Paolo
AU - Farrell, Steven
AU - Spiropulu, Maria
AU - Vlimant, Jean Roch
AU - Aurisano, Adam
AU - Hewes, Jeremy
AU - Cerati, Giuseppe
AU - Gray, Lindsey
AU - Klijnsma, Thomas
AU - Kowalkowski, Jim
AU - Atkinson, Markus
AU - Neubauer, Mark
AU - Dezoort, Gage
AU - Thais, Savannah
AU - Ballow, Alexandra
AU - Lazar, Alina
AU - Caillou, Sylvain
AU - Rougier, Charline
AU - Stark, Jan
AU - Vallier, Alexis
AU - Sardain, Jad
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
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U2 - 10.1088/1742-6596/2438/1/012117
DO - 10.1088/1742-6596/2438/1/012117
M3 - Conference article
AN - SCOPUS:85149744457
SN - 1742-6588
VL - 2438
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012117
T2 - 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2021
Y2 - 29 November 2021 through 3 December 2021
ER -