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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
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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.
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Cited by 1 Pith paper
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Transformer networks for Heavy flavor jet tagging
A review of transformer-based jet tagging that highlights the authors' CA-Mixer network as a state-of-the-art, faster alternative to Particle Transformer.
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