Application of Machine Learning Based Top Quark and W Jet Tagging to Hadronic Four-Top Final States Induced by SM as well as BSM Processes

Abstract

We apply gradient boosting machine learning techniques to the problem of hadronic jet substructure recognition using classical subjettiness variables available within a common parameterized detector simulation package DELPHES. Per-jet tagging classification is being explored. Jets produced in simulated proton-proton collisions are identified as consistent with the hypothesis of coming from the decay of a top quark or a W boson and are used to reconstruct the mass of a hypothetical scalar resonance decaying to a pair of top quarks in events where in total four top quarks are produced. Results are compared to the case of a simple cut-based tagging technique for the stacked histograms of a mixture of a Standard Model as well as the new physics process.