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We present an improved hybrid algorithm for vertexing, that combines deeplearning with conventional methods. Even though the algorithm is a genericapproach to vertex finding, we focus here on it's application as an alternativePrimary Vertex (PV) finding tool for the LHCb experiment.  In the transition to Run 3 in 2021, LHCb will undergo a major luminosityupgrade, going from 1.1 to 5.6 expected visible PVs per event, and it willadopt a purely software trigger. We use a custom kernel to transform the sparse3D space of hits and tracks into a dense 1D dataset, and then apply DeepLearning techniques to find PV locations using proxy distributions to encodethe truth in training data. Last year we reported that training networks on ourkernels using several Convolutional Neural Network layers yielded better than90 % efficiency with no more than 0.2 False Positives (FPs) per event.Modifying several elements of the algorithm, we now achieve better than 94 %efficiency with a significantly lower FP rate. Where our studies to date havebeen made using toy Monte Carlo (MC), we began to study KDEs produced fromcomplete LHCb Run 3 MC data, including full tracking in the vertex locatorrather than proto-tracking.

An updated hybrid deep learning algorithm for identifying and locating primary vertices