Finding the Right Primary Vertex Using Machine Learning

We use a binary classification neural network to find the primary vertex relating to a bottom quark decay using information on the number of tracks measured and certain input variables. Different machine learning packages were explored and models were built using XGboost, Keras and PyTorch. A final model achieved an accuracy of 78.18% on LHCb run 2 data.