CERN Accelerating science

ATLAS Slides
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.



 記錄創建於2024-10-28,最後更新在2024-10-30


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