| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01349 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202533701349 | |
| Published online | 07 October 2025 | |
https://doi.org/10.1051/epjconf/202533701349
Towards Machine-Learning Particle Flow with the ATLAS Detector at the LHC
1 University of Bologna, Department of Physics and Astronomy “Augusto Righi”
2 National Institute for Nuclear Physics (INFN), Bologna
* e-mail: luca.clissa2@unibo.it
Published online: 7 October 2025
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 at the LHC. 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 are also discussed.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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