Work

Enhancing the OMTF Trigger for Phase-2 with a GNN

Year: 2025

Type: article

Abstract: This poster presents our work to interpret track segments (stubs) from the muon detectors as nodes of a graph, 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. This poster was presented at the 7th Inter-Experimental LHC Machine Learning Workshop (19–23 May 2025, CERN).

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15470853

Author: Cardini, Andrea

Institution: Universidad de Oviedo

Language: English

Topic: Particle Detector Development and Performance

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Agencia Estatal de Investigación

Awards: G2361494173, G6837507712