New Deep Learning based approach to Primary Vertex finding in ATLAS experiment

In this current research, we explore the potential of deep neural networks (DNNs) for the identification and precise localization of primary vertices (PVs) within proton-proton collisions at the LHC. This innovative approach, termed as PV-Finder, adopts a hybrid methodology, initiating with kernel density estimators (KDEs), analytically calculated from a heuristic ensemble of charged track parameters, which serve as input for the DNN alongside ground truth PV information, facilitating the extraction of PV positions. The neural networks undergo rigorous training on an extensive ATLAS simulated dataset, with subsequent evaluation on an independent test dataset. To validate the efficacy of our algorithm, we conducted a comparative analysis against the standard vertex finder algorithm in ATLAS. We also share our ongoing work and future plans to develop an ‘end-to-end’ tracks-to-histograms DNN where we replace analytical calculation of KDEs with fully connected NN layers. Our research strives to make impactful contributions to the continuous evolution of data analysis and machine learning techniques within the realm of High Energy Physics.