Speaker
Andrea Cardini
(Universidad de Oviedo)
Description
This talk presents our work to interpret track segments (stubs) from the muon detectors as nodes of a graph and analyze their structure and reconstruct the muon trajectory using a Graph Neural Network (GNN). As a case study, we focus on the barrel-endcap transition region of the CMS experiment. This GNN also aims at the reduction of computing time, allowing for the integration within the upgraded hardware-level trigger. A preliminary implementation on firmware will also be discussed. The GNN model is being adapted for targeting also displaced muon signatures to potentially enhance searches for long-lived particles.
| Would you like to be considered for an oral presentation? | Yes |
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Authors
Andrea Cardini
(Universidad de Oviedo)
Clara Ramon Alvarez
(University of Virginia (US))
Pelayo Leguina
(Universidad de Oviedo (ES))
Dr
Pietro Vischia
(Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA))
Santiago Folgueras
(Universidad de Oviedo (ES))