Full event interpretation with machine-learning-based particle-flow reconstruction in the CMS detector
Yazarlar (1)
Prof. Dr. Hasan OĞUL Sinop Üniversitesi, Türkiye
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Özgün Makale
(Uluslararası alan indekslerindeki dergilerde yayınlanan tam makale)
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| Dergi Adı | European Physical Journal C | ||
| Dergi ISSN | 1434-6044 Dergi Bilgileri (2026) | ||
| Makale Dili | – | Basım Tarihi | 02-2026 |
| Kabul Tarihi | 23-04-2026 | Yayınlanma Tarihi | 31-08-2026 |
| Cilt / Sayı / Sayfa | 86 / 8 / 1025–0 | DOI | 10.1140/epjc/s10052-026-15754-5 |
| Makale Linki | https://hal.science/hal-05491247/ | ||
| UAK Araştırma Alanları |
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| Özet |
| The particle-flow (PF) algorithm constructs a global description of each particle collision by producing a comprehensive list of final-state particles, and is central to event reconstruction in the CMS experiment at the CERN LHC. The existing PF implementation relies on physics-motivated heuristics and assumptions that can be replaced by machine-learning (ML) models trained directly on simulated data and naturally suited to modern graphics processing units (GPUs). A state-of-the-art ML-based PF (MLPF) reconstruction algorithm, implemented within the CMS software framework, is presented. The MLPF algorithm performs a learnable full-event reconstruction on GPUs, generalizes across detector conditions and collision energies, and replaces multiple modular reconstruction steps with a single unified model. Physics performance comparable to standard PF reconstruction is achieved in both simulation and data, with improved jet energy resolution and inference time. In simulated top quark-antiquark events under LHC Run-3 (20232024) conditions, the jet energy resolution improves by 1020% for jets with transverse momentum between 30100 GeV. Inference time is evaluated using simulated multijet events, with a median of 20 ms per event on an Nvidia L4 GPU, compared to approximately 110 ms for the standard CMS PF reconstruction. |
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BM Sürdürülebilir Kalkınma Amaçları
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| Google Scholar | 1 |