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We are studying the use of deep neural networks (DNNs) to identify and locateprimary vertices (PVs) in proton-proton collisions at the LHC. Earlier workfocused on finding primary vertices in simulated LHCb data using a hybridapproach that started with kernel density estimators (KDEs) derivedheuristically from the ensemble of charged track parameters and predicted"target histogram" proxies, from which the actual PV positions are extracted.We have recently demonstrated that using a UNet architecture performsindistinguishably from a "flat" convolutional neural network model. We havedeveloped an "end-to-end" tracks-to-hist DNN that predicts target histogramsdirectly from track parameters using simulated LHCb data that provides betterperformance (a lower false positive rate for the same high efficiency) than thebest KDE-to-hists model studied. This DNN also provides better efficiency thanthe default heuristic algorithm for the same low false positive rate."Quantization" of this model, using FP16 rather than FP32 arithmetic, degradesits performance minimally. Reducing the number of UNet channels degradesperformance more substantially. We have demonstrated that the KDE-to-histsalgorithm developed for LHCb data can be adapted to ATLAS and ACTS data usingtwo variations of the UNet architecture. Within ATLAS/ACTS, these algorithmshave been validated against the standard vertex finder algorithm. Bothvariations produce PV-finding efficiencies similar to that of the standardalgorithm and vertex-vertex separation resolutions that are significantlybetter.

Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC