What question did this study set out to answer?

The research aims to enhance the identification of hadronic jets from top quarks and W bosons in complex collision events.

February 8, 2026

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

Key Points

The research aims to enhance the identification of hadronic jets from top quarks and W bosons in complex collision events.
Utilized cut-based and machine learning techniques for jet tagging.
Analyzed jets from simulated proton-proton collisions.
Focused on jets revealing decay signatures of top quarks and W bosons.
Reconstructed invariant mass of a hypothetical scalar resonance decaying into top quarks.
Improved recognition of jet substructure utilizing classical variables and machine learning.
Identified effective tagging techniques that distinguish signal from background noise.
Enabled comparison of resonance properties against background in four-top events.

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 y 0 decaying into a pair of top quarks. We reconstruct the resonance invariant mass and compare its properties over the falling background using the two tagging approaches, with implications to LHC searches

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