Resonant production of vectorlike quarks at the HL-LHC

We investigate the potentially observable consequences of resonant production of vectorlike quarks through a diquark scalar, $S_{uu}$, in proton-proton collisions at the LHC energy. For this study, we consider the vectorlike quark decay into a $W^{+}$ boson and a $b$ quark, leading to a 6-jet final state. We focus on studying the 7 and 8.5 TeV mass $S_{uu}$, for 13 and 13.6 TeV LHC, analyzing its signature particularly through the ATLAS and CMS detectors. Based on comprehensive Monte Carlo (MC) simulations, we developed a dedicated Machine Learning (ML) algorithm for separating signal events from the associated background. Three different models are described (Boosted Decision Tree, Random Forest and Neural Networks), choosing the most efficient one. We show that the background can be reduced to a negligible level by choosing high-purity working points. A signal-background discriminant is defined based on all variables used in the ML algorithm.