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ATLAS Collaboration", "creators": [ { "affiliations": [ { "id": "01ggx4157", "identifiers": [ { "identifier": "01ggx4157", "scheme": "ror" } ], "name": "European Organization for Nuclear Research" } ], "person_or_org": { "family_name": "Renusch", "given_name": "Jonathan", "name": "Renusch, Jonathan", "type": "personal" }, "role": { "id": "researcher", "title": { "de": "WissenschaftlerIn", "en": "Researcher" } } } ], "description": "

The High Luminosity Large Hadron Collider (HL-LHC), scheduled to begin operation after 2030, will increase the number of proton-proton collisions per event from approximately 60 to up to 200. This rise in interaction density will substantially elevate the occupancy within the ATLAS Muon Spectrometer, necessitating more efficient and robust real-time data processing strategies for the Event Filter. To address these challenges on the algorithmic side, we present a comparative study of state-of-the-art graph neural network machine learning (ML) architectures designed to reject hits from background sources such as hadronic punch-throughs and gamma radiation within the ATLAS experiment’s Muon Spectrometer. These architectures are designed to assist baseline reconstruction algorithms while maintaining or improving physics performance. A central contribution of our work is the use of a novel Fourier series-inspired encoding scheme for continuous input features. Across all tested ML architectures, our Fourier encoding yields improvements in noise rejection and achieves lower computational cost compared to traditional ML encoding types such as linear and other hybrid methods. In addition, we show first results on the use of Masked Transformer models for end-to-end muon tracking within the Muon Spectrometer as an alternative approach to the baseline reconstruction. These models are inspired by recent breakthroughs in computer vision, in particular detection.

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