SciPost Submission Page
Graph Neural Network Acceleration on FPGAs for Fast Inference in Future Muon Triggers at HL-LHC
by Martino Errico, Davide Fiacco, Stefano Giagu, Giuliano Gustavino, Valerio Ippolito, Graziella Russo
Submission summary
| Authors (as registered SciPost users): | Martino Errico |
| Submission information | |
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| Preprint Link: | scipost_202509_00064v1 (pdf) |
| Date submitted: | Sept. 30, 2025, 6:57 p.m. |
| Submitted by: | Martino Errico |
| Submitted to: | SciPost Physics Proceedings |
| Proceedings issue: | The 2nd European AI for Fundamental Physics Conference (EuCAIFCon2025) |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approaches: | Experimental, Computational |
The author(s) disclose that the following generative AI tools have been used in the preparation of this submission:
GPT-5, for non-essential editing and LaTeX syntax suggestions
Abstract
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.