Report number
| ATL-SOFT-SLIDE-2024-507 |
Title
| Towards Machine-Learning Particle Flow with the ATLAS Detector at the LHC |
Author(s)
| Clissa, Luca (Universita e INFN, Bologna (IT)) ; Swiatlowski, Maximilian J (TRIUMF (CA)) ; Vivarelli, Iacopo (Universita e INFN, Bologna (IT)) ; Bohm, Jessica (TRIUMF (CA)) ; Jovanovic, Marko ; Himmens, Joshua Jon (TRIUMF (CA)) |
Corporate author(s)
| The ATLAS collaboration |
Collaboration
| ATLAS Collaboration |
Submitted to
| 27th International Conference on Computing in High Energy & Nuclear Physics, Kraków, Pl, 19 - 25 Oct 2024 |
Submitted by
| luca.clissa@cern.ch on 28 Oct 2024 |
Subject category
| Particle Physics - Experiment |
Accelerator/Facility, Experiment
| CERN LHC ; ATLAS |
Free keywords
| Particle flow ; Particle identification ; Energy calibration ; Machine Learning ; Deep Learning for point cloud |
Abstract
| Particle flow reconstruction at colliders combines various detector subsystems (typically the calorimeter and tracker) to provide a combined event interpretation that utilizes the strength of each detector. The accurate association of redundant measurements of the same particle between detectors is the key challenge in this technique. This contribution describes recent progress in the ATLAS experiment towards utilizing machine-learning to improve particle flow in the ATLAS detector. In particular, point-cloud techniques are utilized to associate measurements from the same particle, leading to reduced confusion compared to baseline techniques. Next steps towards further testing and implementation will be discussed. |