Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.26419 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908569053429760 |
|---|---|
| author | Errico, Martino Fiacco, Davide Giagu, Stefano Gustavino, Giuliano Ippolito, Valerio Russo, Graziella |
| author_facet | Errico, Martino Fiacco, Davide Giagu, Stefano Gustavino, Giuliano Ippolito, Valerio Russo, Graziella |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26419 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph Neural Network Acceleration on FPGAs for Fast Inference in Future Muon Triggers at HL-LHC Errico, Martino Fiacco, Davide Giagu, Stefano Gustavino, Giuliano Ippolito, Valerio Russo, Graziella High Energy Physics - Experiment 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. |
| title | Graph Neural Network Acceleration on FPGAs for Fast Inference in Future Muon Triggers at HL-LHC |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2509.26419 |