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 tt¯ pair. The enhanced coupling not only dictates that the dominant decay mode would be to a tt¯ pair, but also to a very large G(1) width, necessitating the use of a renormalized G(1) propagator. This, along with the presence of large backgrounds (specially tt¯jj), 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. Published by the American Physical Society 2024
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