The High-Luminosity LHC (HL-LHC) will reach luminosities up to 7 times higher than the previous run, yielding denser events and larger occupancies. Next generation trigger algorithms must retain reliable selection within a strict latency budget. This work explores machine-learning approaches for future muon triggers, using the ATLAS Muon Spectrometer as a benchmark. A Convolutional Neural Network (CNN) is used as a reference, while a Graph Neural Network (GNN) is introduced as a natural model for sparse detector data. Preliminary single-track studies show that GNNs achieve high efficiency with compact architectures, an encouraging result in view of FPGA deployment.
@article{arxiv.2509.26419,
title = {Graph Neural Network Acceleration on FPGAs for Fast Inference in Future Muon Triggers at HL-LHC},
author = {Martino Errico and Davide Fiacco and Stefano Giagu and Giuliano Gustavino and Valerio Ippolito and Graziella Russo},
journal= {arXiv preprint arXiv:2509.26419},
year = {2025}
}
Comments
5 pages, 2 figures Submission to SciPost for conference proceedings