Particle Detector Development and Performance
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Abstract
Abstract
The LHCb detector at the Large Hadron Collider has been upgraded to acquire an unprecedented 32 Tbps of particle-collision data to provide new insights in the High Energy Physics domain. The data produced by the detector is filtered in real-time to select interesting collisions. As part of the upgrade, a pre-filtering stage has been removed leading to a factor 40 increase in data rate. To deal with the high throughput demands of LHCb real-time data processing, we present an off-the-shelf network architecture using zero-copy techniques in conjunction with an efficient, fully-GPU-based filter. Our converged architecture is able to process the full 32 Tbps of particle-collision data in real-time, the highest in any physics experiment to date. Our result extends the reach of the LHCb physics programme and sets a new standard for real-time data processing at particle physics experiments.
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@article{Aaij2026converged,
title = {A converged architecture for processing 32 Tbps of physics data in real-time at the LHCb experiment},
author = {Roel Aaij and Christina Agapopoulou and Thomas Boettcher and Dorothea vom Bruch and D. Pérez and A J Vidal and Tommaso Colombo and D C Craik and Tim Evans and Placido Fernandez Declara and Marianna Fontana and Vladimir V. Gligorov and Arthur Hennequin and Louis Henry and Brij Kishor Jashal and S. Mariani and Rosen Matev and Niklas Nolte and Niko Neufeld and Arantza Oyanguren and Alberto Perro and Flavio Pisani and Renato Quagliani and Florian Reiss and Kate A. Richardson and Alessandro Scarabotto and R Schwemmer and P Spradlin and Marian Stahl and Jiahui Zhuo},
journal = {arXiv (Cornell University)},
year = {2026},
doi = {10.48550/arxiv.2607.21492},
url = {https://doi.org/10.48550/arxiv.2607.21492}
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