A graph neural network (ETX4VELO) reconstructs LHCb VELO tracks with performance comparable to the production 'search by triplet' algorithm while running end to end in the GPU-based first-level trigger, with additional FPGA power and throughput comparisons.
Development of the Topological Trigger for LHCb Run 3
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abstract
The data-taking conditions expected in Run 3 of the LHCb experiment at CERN are unprecedented and challenging for the software and computing systems. Despite that, the LHCb collaboration pioneers the use of a software-only trigger system to cope with the increased event rate efficiently. The beauty physics programme of LHCb is heavily reliant on topological triggers. These are devoted to selecting beauty-hadron candidates inclusively, based on the characteristic decay topology and kinematic properties expected from beauty decays. The following proceeding describes the current progress of the Run 3 implementation of the topological triggers using Lipschitz monotonic neural networks. This architecture offers robustness under varying detector conditions and sensitivity to long-lived candidates, improving the possibility of discovering New Physics at LHCb.
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Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous Architectures
A graph neural network (ETX4VELO) reconstructs LHCb VELO tracks with performance comparable to the production 'search by triplet' algorithm while running end to end in the GPU-based first-level trigger, with additional FPGA power and throughput comparisons.