Speaker
Description
The LHCb experiment at the Large Hadron Collider (LHC) operates a fully software-based trigger system that processes proton-proton collisions at a rate of 30 MHz, reconstructing both charged and neutral particles in real time. The first stage of this trigger system, running on approximately 500 GPU cards, performs a track pattern recognition to reconstruct particle trajectories with low latency.
Starting with the 2025 data-taking period, a novel approach has been introduced for precise track parameter estimation: a custom Kalman Filter, highly optimized for GPU execution, is now employed to fit tracks at the full collision rate of 30 MHz. This implementation leverages dedicated parametrizations of material interactions and the magnetic field to meet stringent throughput requirements.
This is the first time such a high-precision track fitting is performed at the full LHC collision frequency in any experiment. The result is a marked improvement in the momentum and mass resolution, increased robustness against detector misalignments, and a reduced rate of fake tracks. Moreover, this development represents a critical step toward future full event reconstruction at the LHC collision rate.
In this talk, we will outline the requirements for real-time track fitting in LHCb’s first-level trigger, detail the implementation of the Kalman Filter on GPUs, and present a comprehensive performance evaluation using the 2025 data set.
Significance
This talk presents the implementation and performance of a Kalman Filter for track fitting on GPUs at 30MHz, which is the first time a Kalman Filter is used at the full LHC collision frequency.
| Experiment context, if any | LHCb |
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