[2603.25793] Vision Transformers and Graph Neural Networks for Charged Particle Tracking in the ATLAS Muon Spectrometer

Physics > Data Analysis, Statistics and Probability arXiv:2603.25793 (physics) [Submitted on 26 Mar 2026] Title:Vision Transformers and Graph Neural Networks for Charged Particle Tracking in the ATLAS Muon Spectrometer Authors:Jonathan Renusch (on behalf of the ATLAS Collaboration) View a PDF of the paper titled Vision Transformers and Graph Neural Networks for Charged Particle Tracking in the ATLAS Muon Spectrometer, by Jonathan Renusch (on behalf of the ATLAS Collaboration) View PDF HTML (experimental) Abstract:The identification and reconstruction of charged particles, such as muons, is a main challenge for the physics program of the ATLAS experiment at the Large Hadron Collider. This task will become increasingly difficult with the start of the High-Luminosity LHC era after 2030, when the number of proton-proton collisions per bunch crossing will increase from 60 to up to 200. This elevated interaction density will also increase the occupancy within the ATLAS Muon Spectrometer, requiring more efficient and robust real-time data processing strategies within the experiment's trigger system, particularly the Event Filter. To address these algorithmic challenges, we present two machine-learning-based approaches. First, we target the problem of background-hit rejection in the Muon Spectrometer using Graph Neural Networks integrated into the non-ML baseline reconstruction chain, demonstrating a 15 % improvement in reconstruction speed (from 255 ms to 217 ms). Second, we pres...