SciPost Submission Page
Using AI on FPGAs for the CMS Overlap Muon Track Finder for the HL-LHC
by Pelayo Leguina, Santiago Folgueras, Andrea Cardini, Elena Aller
Submission summary
| Authors (as registered SciPost users): | Pelayo Leguina |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2509.23347v1 (pdf) |
| Date submitted: | Sept. 30, 2025, 8:30 a.m. |
| Submitted by: | Pelayo Leguina |
| 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 |
Abstract
Operating the CMS Level-1 trigger under the intense conditions of the High-Luminosity Large Hadron Collider -- with approximately 63~Tb/s of input and a fixed 12.5~$\mu$s latency -- poses a demanding real-time reconstruction challenge. The CMS muon system is organized into three regions: a barrel, an endcap, and the intermediate barrel-endcap ``overlap'' region. In this overlap transition, the Overlap Muon Track Finder can be suboptimal for displaced-muon and long-lived-particle signatures. We present a first approach to a graph neural network tailored to these constraints, using GraphSAGE layers and a compact multi-layer perceptron to regress the inverse transverse momentum of muons. A PyTorch to C++ and high-level synthesis flow demonstrates feasibility, with initial results showing good agreement with simulation. Although a fully parallel implementation would exceed available field-programmable gate array resources, quantization, pruning, and multiplier reuse point the way toward a practical Phase-2 deployment.