Performance of a geometric deep learning pipeline for HL-LHC particle tracking

Performance of a geometric deep learning pipeline for HL-LHC particle tracking

Abstract The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX’s tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML...

Full description

Bibliographic Details
Main Authors: Xiangyang Ju, Daniel Murnane, Paolo Calafiura, Nicholas Choma, Sean Conlon, Steven Farrell, Yaoyuan Xu, Maria Spiropulu, Jean-Roch Vlimant, Adam Aurisano, Jeremy Hewes, Giuseppe Cerati, Lindsey Gray, Thomas Klijnsma, Jim Kowalkowski, Markus Atkinson, Mark Neubauer, Gage DeZoort, Savannah Thais, Aditi Chauhan, Alex Schuy, Shih-Chieh Hsu, Alex Ballow, Alina Lazar
Format: Article
Language:English
Published: SpringerOpen 2021-10-01
Series:European Physical Journal C: Particles and Fields
Online Access:https://doi.org/10.1140/epjc/s10052-021-09675-8

Internet

https://doi.org/10.1140/epjc/s10052-021-09675-8

Similar Items