  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.
