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Jiahui Zhuo

Publications and source records attributed to Jiahui Zhuo.

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A converged architecture for processing 32 Tbps of physics data in real-time at the LHCb experiment

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.

hep-ex

Seeding and Matching algorithms for the first GPU-based High Level Trigger of the LHCb experiment

We describe the GPU implementation of the Seeding and Matching algorithms, developed for the first level trigger of the LHCb experiment and key to reconstruct long and very displaced tracks at 40 MHz. The algorithms have been participating in the data taking during the full Run 3 of LHCb with a very high throughput, increasing the physics reach of the experiment. The Seeding is a standalone pattern recognition algorithm aiming at finding charged particle trajectories in the most forward tracker of LHCb. These trajectories are then extrapolated backward by the Matching algorithm which combines them with stubs formed from hits in the first tracker in order to form what we call Long tracks. Hits in the second tracker are then searched for to better define the trajectory and improve the track momentum resolution. This backward approach, complementary to the approach of extrapolating the stubs in the first detector to the forward tracker through the magnetic field, improves the Long track efficiency at low transverse momenta, increasing the potential of key physics decay channels.

physics.ins-det

Energy efficiency of a GPU-based computing system for High Energy Physics experiments

In this paper we introduce the energy efficiency as a new metric for evaluating both hardware platforms based on Graphic Processor Units (GPU), and algorithm optimisations at High Energy Physics (HEP) experiments. We develop a method to compute the energy efficiency for the case of the first high level trigger (HLT1) of the LHCb experiment, relating the throughput with GPU specifications such as the number of cores, clock frequency, memory bandwidth and thermal design power. The model can be extended to other HEP experiments to make decisions and reach sustainable computing ecosystems.

hep-ex

LHCb potential to discover long-lived new physics particles with lifetimes above 100 ps

For years, it has been believed that the main LHC detectors can only restrictively play the role of a lifetime frontier experiment exploring the parameter space of long-lived particles (LLPs) - hypothetical particles with tiny couplings to the Standard Model. This paper demonstrates that the LHCb experiment may become a powerful lifetime frontier experiment if it uses the new Downstream algorithm reconstructing tracks that do not let hits in the LHCb vertex tracker. In particular, for many LLP scenarios, LHCb may be as sensitive as the proposed experiments beyond main LHC detectors for various LLP models, including heavy neutral leptons, dark scalars, dark photons, and axion-like particles.

hep-ph