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Using deep neural networks to identify and locate proton-proton collisionpoints, or primary vertices, in LHCb has been studied for several years.Preliminary results demonstrated the ability for a hybrid deep learningalgorithm to achieve similar or better physics performances compared tostandard heuristic approaches. The previously studied architectures relieddirectly on hand-calculated Kernel Density Estimators (KDEs) as input features.Calculating these KDEs was slow, making use of the DNN inference engines in theexperiment's real-time analysis (trigger) system problematic. Here we presentrecent results from a high-performance hybrid deep learning algorithm that usestrack parameters as input features rather than KDEs, opening the path todeployment in the real-time trigger system.

Comparing and improving hybrid deep learning algorithms for identifying and locating primary vertices