Graph Neural Network Acceleration on FPGAs for Real-Time Muon Triggering at the HL-LHC

Le résumé fourni par la source

The upcoming High Luminosity phase of the Large Hadron Collider requires significant advancements in real-time data processing to handle the increased event rates and maintain high-efficiency trigger decisions. In this work, we explore the acceleration of graph neural networks for fast inference in the Level-0 muon trigger system of the ATLAS experiment. Graph-based architectures offer a natural way to represent and process detector hits while preserving spatial and topological information, making them particularly suitable for muon reconstruction in a noisy and sparse environment. Performance is compared with more conventional pattern recognition algorithms currently under development for the future muon trigger system. This work contributes to the broader goal of integrating AI-driven solutions into high-energy physics trigger systems and represents a step forward in enabling hardware-optimised, graph-based inference for real-time event selection in experimental physics.

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