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

TL;DR

This paper demonstrates the use of gradient boosting machine learning techniques for top quark and W boson jet tagging in hadronic final states, improving identification in complex collider events.

Contribution

It introduces a machine learning approach for jet substructure recognition using subjettiness variables, enhancing tagging accuracy over traditional cut-based methods.

Findings

Machine learning improves jet tagging performance.

ML-based tagging outperforms cut-based techniques.

Effective reconstruction of scalar resonance mass.

Abstract

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.