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Register a new userThe Level-0 Muon Trigger for the LHCb Experiment
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A very compact architecture has been developed for the first level Muon Trigger of the LHCb experiment that processes 40 millions of proton-proton collisions per second. For each collision, it receives 3.2 kBytes of data and it finds straight tracks within a 1.2 microseconds latency. The trigger implementation is massively parallel, pipelined and fully synchronous with the LHC clock. It relies on 248 high density Field Programable Gate arrays and on the massive use of multigigabit serial link transceivers embedded inside FPGAs.
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The LHCb experiment at CERN is undergoing an upgrade in preparation for the Run 3 data taking period of the LHC. As part of this upgrade the trigger is moving to a fully software implementation operating at the LHC bunch crossing rate. We present an evaluation of a CPU-based and a GPU-based implementation of the first stage of the High Level Trigger. After a detailed comparison both options are found to be viable. This document summarizes the performance and implementation details of these options, the outcome of which has led to the choice of the GPU-based implementation as the baseline.
The NA62 experiment at CERN SPS aims to measure the Branching Ratio of the very rare kaon decay K+ -> pi+ nu nubar collecting O(100) events with a 10% background to make a stringent test of the Standard Model. One of the main backgrounds to the proposed measurement is represented by the K+ -> pi+ pi0 decay. To suppress this background an efficient photo veto system is foreseen. In the 1-10 mrad angular region the NA48 high performance liquid krypton electromagnetic calorimeter is used. The design, implementation and current status of the Liquid Krypton Electromagnetic Calorimeter Level 0 Trigger are presented.
The LHCb experiment will operate at a luminosity of $2times10^{33}$ cm$^{-2}$s$^{-1}$ during LHC Run 3. At this rate the present readout and hardware Level-0 trigger become a limitation, especially for fully hadronic final states. In order to maintain a high signal efficiency the upgraded LHCb detector will deploy two novel concepts: a triggerless readout and a full software trigger.
The main b-physics trigger algorithm used by the LHCb experiment is the so-called topological trigger. The topological trigger selects vertices which are a) detached from the primary proton-proton collision and b) compatible with coming from the decay of a b-hadron. In the LHC Run 1, this trigger, which utilized a custom boosted decision tree algorithm, selected a nearly 100% pure sample of b-hadrons with a typical efficiency of 60-70%; its output was used in about 60% of LHCb papers. This talk presents studies carried out to optimize the topological trigger for LHC Run 2. In particular, we have carried out a detailed comparison of various machine learning classifier algorithms, e.g., AdaBoost, MatrixNet and neural networks. The topological trigger algorithm is designed to select all interesting decays of b-hadrons, but cannot be trained on every such decay. Studies have therefore been performed to determine how to optimize the performance of the classification algorithm on decays not used in the training. Methods studied include cascading, ensembling and blending techniques. Furthermore, novel boosting techniques have been implemented that will help reduce systematic uncertainties in Run 2 measurements. We demonstrate that the reoptimized topological trigger is expected to significantly improve on the Run 1 performance for a wide range of b-hadron decays.
The integrated low-level trigger and data acquisition (TDAQ) system of the NA62 experiment at CERN is described. The requirements of a large and fast data reduction in a high-rate environment for a medium-scale, distributed ensemble of many different sub-detectors led to the concept of a fully digital integrated system with good scaling capabilities. The NA62 TDAQ system is rather unique in allowing full flexibility on this scale, allowing in principle any information available from the detector to be used for triggering. The design concept, implementation and performances from the first years of running are illustrated.
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