2024/04/05 by Debajyoti Choudhury, Kuldeep Deka, Choudhury, Debajyoti +3 · 3 citations
Computer Science · Physics and Astronomy · Social Sciences · #Distributed and Parallel Computing Systems #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #High Energy Physics - Theory (hep-th) #International Science and Diplomacy #Particle physics theoretical and experimental studies
paper · pdf · doi:10.48550/arxiv.2404.04409
openalex publication_date 2024/04/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 t pair. The enhanced coupling not only dictates that the dominant decay mode would be to a t t 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 t 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.