Scenarios seeking to address the issue of electroweak symmetry breaking often have heavy colored gauge bosons coupling preferentially to the top quark. Considering the bulk Randall-Sundrum as a typical example, we consider the prospects of the first Kaluza-Klein mode (G⁽¹⁾) of the gluon being produced at the LHC in association with a t t̄ pair. The enhanced coupling not only dictates that the dominant decay mode would be to a t t̄ pair, but also to a very large G⁽¹⁾ width, necessitating the use of a renormalised G⁽¹⁾ propagator. This, alongwith the presence of large backgrounds (specially t t̄ j j), renders a conventional cut-based analysis ineffective, yielding only marginal significances of only around 2σ. The use of Machine Learning (ML) techniques alleviates this problem to a great extent. In particular, the use of Artificial Neural Networks helps us identify the most discriminating observables, thereby allowing a significance in excess of 4σ for G⁽¹⁾ masses of ~ 4 TeV.
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Choudhury et al. (2024) studied this question.
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