Application of Machine Learning Based Top Quark and W Jet Tagging to Hadronic Four-Top Final States Induced by SM and BSM Processes (2410.13904v1)

Abstract: We apply both cut-based and machine learning techniques using the same inputs to the challenge of hadronic jet substructure recognition, utilizing classical subjettiness variables within the Delphes parameterized detector simulation framework. We focus on jets generated in simulated proton-proton collisions, identifying those consistent with the decay signatures of top quarks or W bosons. Such jets are employed in four-top quark events in fully hadronic final states stemming from both the Standard Model as well as from a new physics process of a hypothetical scalar resonance y0 decaying into a pair of top quarks. We reconstruct the resonance invariant mass and compare it properties over the falling background using the two tagging approaches, with implications to LHC searches.