Real-time lepton identification at LHCb in Run 3 using Lipschitz neural networks
Abstract
The LHCb physics program in Run 3 relies critically on the efficient real-time selection of events containing muons and electrons, which are key signatures in a wide range of heavy-flavor and exotic decay processes. The LHCb Run 3 detector now operates with a fully software-based trigger that processes the complete detector readout at the LHC bunch-crossing rate, with the first trigger stage executed on GPUs. In this environment, particle-identification algorithms must achieve high efficiency and background rejection while satisfying stringent constraints on throughput and memory footprint. We present algorithms for muon and electron identification in the LHCb Run 3 GPU trigger based on Lipschitz-constrained neural networks. Separate networks are developed for muons and electrons and are trained using simulated events. Their performance is evaluated relative to the previous baseline algorithms, demonstrating improved discrimination across a wide range of kinematic regions while remaining compatible with the requirements of real-time GPU execution.
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BibTeX
@misc{vidal2026realtime,
title = {{Real-time lepton identification at LHCb in Run 3 using Lipschitz neural networks}},
author = {Adrian Casais Vidal and Kate A. Richardson and Maarten Van Veghel and Marco Santimaria and Marianna Fontana and Mike Williams},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.07903},
eprint = {2609.07903},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.07903},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Real-time lepton identification at LHCb in Run 3 using Lipschitz neural networks
AU - Adrian Casais Vidal
AU - Kate A. Richardson
AU - Maarten Van Veghel
AU - Marco Santimaria
AU - Marianna Fontana
AU - Mike Williams
PY - 2026
DA - 2026/09/07
PB - arXiv
DO - 10.48550/arxiv.2609.07903
UR - https://arxiv.org/abs/2609.07903
AB - The LHCb physics program in Run 3 relies critically on the efficient real-time selection of events containing muons and electrons, which are key signatures in a wide range of heavy-flavor and exotic decay processes. The LHCb Run 3 detector now operates with a fully software-based trigger that processes the complete detector readout at the LHC bunch-crossing rate, with the first trigger stage executed on GPUs. In this environment, particle-identification algorithms must achieve high efficiency and background rejection while satisfying stringent constraints on throughput and memory footprint. We present algorithms for muon and electron identification in the LHCb Run 3 GPU trigger based on Lipschitz-constrained neural networks. Separate networks are developed for muons and electrons and are trained using simulated events. Their performance is evaluated relative to the previous baseline algorithms, demonstrating improved discrimination across a wide range of kinematic regions while remaining compatible with the requirements of real-time GPU execution.
ER -