Work

Application of machine learning-based top quark and W jet tagging to hadronic four-top final states induced by SM and BSM processes

Year: 2026

Type: article

Abstract: In this paper, 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 [Formula: see text] 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.

Source: Modern Physics Letters A

DOI: 10.1142/s0217732326500665

Authors: Jiří Kvita, Petr Baroň, Radek Přívara, Monika Machalová, Rostislav Vodák, Jan Tomeček

Institutions: Czech Academy of Sciences, FZU - Institute of Physics of the Czech Academy of Sciences, Palacký University Olomouc

Language: English

Topic: Particle physics theoretical and experimental studies

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funder: Grantová Agentura České Republiky

Award: Machine learning applications in high-energy and astroparticle physics