Speakers
Description
The upgraded LHCb experiment is pioneering the landscape of real-time data-processing techniques using an heterogeneous computing infrastructure, composed of both GPUs and FPGAs, aimed at boosting the performance of the HLT1 reconstruction. Amongst the novelties in the reconstruction infrastructure made for the Run 3, the introduction of a real-time hit-finding FPGA-based architecture on the silicon pixel vertex detector (VELO) stands out. For the first time at any LHC experiment, the bi-dimensional clusters of active pixels on the VELO are reconstructed before event-building, directly on the detector readout boards, at the full interaction rate of ~30MHz. In addition to saving HLT1 computing resources and reducing the DAQ bandwidth, the availability of well reconstructed particle hits at the readout level opens up the possibility of further processing in order to reconstruct even more complex quantities. Specifically, measuring hit rates at several positions on the detector sensors yields a novel method to analyse and monitor the geometrical properties of the luminous region in real time. To pursue such idea, a set of programmable counters has been implemented in firmware. This set of counters uses minimal FPGA resources and it provides real-time measurements of instantaneous luminosity and beam spot position, shape and inclination. This is achieved via linearised computations based on principal component analysis (PCA), that are performed during data taking on the LHCb slow control software. This method differs substantially from the usual techniques relying on track and vertex reconstruction, that are prone to misalignment biases and depend on the HLT running conditions. The implementation of such a beamspot reconstruction algorithm has the potential to crucially reduce the time needed to perform vertex reconstruction at LHCb. In this contribution, we describe the implementation of such a system for real-time beam spot measurement, and report the results obtained with proton-proton and lead-lead data collected in 2024 and 2025.
