Using AI on FPGAs for the CMS Overlap Muon Track Finder for the HL-LHC
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911183457484800 |
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| author | Leguina, Pelayo Folgueras, Santiago Cardini, Andrea Aller, Elena |
| author_facet | Leguina, Pelayo Folgueras, Santiago Cardini, Andrea Aller, Elena |
| contents | 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~$μ$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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23347 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Using AI on FPGAs for the CMS Overlap Muon Track Finder for the HL-LHC Leguina, Pelayo Folgueras, Santiago Cardini, Andrea Aller, Elena High Energy Physics - Experiment 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~$μ$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. |
| title | Using AI on FPGAs for the CMS Overlap Muon Track Finder for the HL-LHC |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2509.23347 |