Alexis VALLIER
(Laboratoire des 2 Infinis - Toulouse, CNRS / Univ. Paul Sabatier (FR))
The physics reach of the HL-LHC will be limited by how efficiently the experiments can use the available computing resources, i.e. affordable software and computing are essential. The development of novel methods for charged particle reconstruction at the HL-LHC incorporating machine learning techniques or based entirely on machine learning is a vibrant area of research. In the past years, algorithms for track pattern recognition based on graph neural networks (GNNs) have emerged as a particularly promising approach. We present new algorithms that can handle complex realistic detectors and achieve tracking efficiency and purity similar to production tracking algorithms based on Kalman filters. Crucially for HL-LHC and future collider applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.
Alex Ballow
(Youngstown state university)
Alexis VALLIER
(Laboratoire des 2 Infinis - Toulouse, CNRS / Univ. Paul Sabatier (FR))
Alina Lazar
(Youngstown state university)
Charline Rougier
(Laboratoire des 2 Infinis - Toulouse, CNRS / Univ. Paul Sabatier (FR))
Chun-Yi Wang
(National Tsing Hua University)
Daniel Murnane
(Lawrence Berkeley National Laboratory)
Jad Sardain
(Laboratoire des 2 Infinis - Toulouse, CNRS / Univ. Paul Sabatier (FR))
Jan STARK
Mark Neubauer
(University of Illinois at Urbana Champaign)
Markus Atkinson
(University of Illinois at Urbana Champaign)
Paolo Calafiura
(Lawrence Berkeley National Laboratory)
Shih-Chieh Hsu Hsu
(University of Washington)
Steven Farrell
(Lawrence Berkeley National Laboratory)
Sylvain Caillou
(Laboratoire des 2 Infinis - Toulouse, CNRS / Univ. Paul Sabatier (FR))
Xiangyang Ju
(Lawrence Berkeley National Laboratory)
