One of the biggest challenges for the upgrade of the LHCb RICH detectors from 2020 is to readout the photon detectors at the full 40 MHz rate of the LHC proton-proton collisions. A test facility has been setup at CERN with the purpose to investigate the behaviour of the Multi Anode PMTs, which have been proposed for the upgrade, and their readout electronics at high trigger rates. The MaPMTs are illuminated with a monochromatic laser that can be triggered independently of the readout electronics. A first series of tests, including threshold scans, is performed at low trigger rates (20 kHz) for both the readout and the laser with the purpose to characterise the behaviour of the system under test. Then the trigger rate is increased in two separate steps. First the MaPMTs are exposed to high illumination by triggering the pulsed laser at a high (20 MHz) repetition rate while the DAQ is readout at the same low rate as before. In this way the performance of the MaPMTs and the attached electronics can be evaluated at high laser exposure rate. In the second step both the laser and the DAQ are triggered at the high rate in order to evaluate the full readout chain.
The use of Ring Imaging Cherenkov detectors (RICH) offers a powerful technique for identifying the particle species in particle physics. These detectors produce 2D images formed by rings of individual photons superimposed on a background of photon rings from other particles. The RICH particle identification (PID) is essential to the LHCb experiment at CERN. While the current PID algorithm has performed well during LHC data-taking periods between 2010 to 2018, its complexity poses a challenge for LHCb computing infrastructure upgrades towards multi-core architectures. The high particle multiplicity environment of future LHC runs strongly motivates shifting towards high-throughput computing for the online event reconstruction. In this contribution, we introduce a convolutional neural network (CNN) approach to particle identification in LHCb RICH. The CNN takes binary input images from the two RICH detectors to classify particle species. The input images are polar-transformed sub-sections of the RICH photon-detection planes. The model is hyperparameter-optimised and trained on classification accuracy with simulated collision data for the upcoming LHC operation starting in 2022. The PID performance of the CNN is comparable to the conventional algorithm, and its simplicity renders it suitable for fast online reconstruction through parallel processing. We show that under conditions of reduced combinatorial background, as expected from the introduction of timing resolution to the RICH detectors in future upgrades, the network achieves a particle identification performance close to 100 %, with simultaneous misclassification of the most prevalent particle species approaching 0 %.
During 2019/20 LHCb Upgrade of the Ring Imaging Cherenkov (RICH) system the current Hybrid Photon Detectors (HPDs), with embedded 1 MHz readout electronics, will be replaced with Multi-anode Photomultiplier Tubes (MaPMTs) with new external 40 MHz readout electronics. Two sizes of Hamamatsu 64-channel MaPMT have been selected as the photon detectors: the 1-inch R13742 and the 2-inch R13743 MaPMTs, custom modifications of the models R11625 and R12699. Including spares, 3100 R13742 and 450 R13743 are purchased. The campaign to characterise all units, to ensure compliance with minimum specifications and to allow for selection of units with similar operational parameters is ongoing. The key characteristics comprise the average gain, the spread of the gain (uniformity), the peak-to-valley ratio, the dark count rate as well as the dependency of the gain on the high voltage (k-factor). So far 474 and 45 units have been tested, respectively. The test results will be presented. Additional measurements and studies, made with subsets of MaPMT, round the picture: the Quantum Efficiency, the loss of photon detection efficiency in magnetic fields and minimal mu-metal shield configurations to effectively shield them up to 3 mT.