Probing a GeV-scale Scalar Boson in Association with a TeV-sacle Vector-like Quark in the U(1)_T3R BSM Extension at the Large Hadron Collider using Machine Learning

A model based on a U(1)_T3R extension of the Standard Model can address the mass hierarchy between the third and the first two generations of fermions, explain thermal dark matter abundance, and the muon g - 2 and B-meson anomalies. The model predicts a light scalar boson φ' and a heavy vector-like quark χ_u that can be probed from proton-proton (pp) collisions at CERN's Large Hadron Collider (LHC). We develop a discovery methodology considering the production of χ_u and φ' from g-g and t-χ_u fusion processes and final states of the χ_u decaying to b-quarks, muons, and MET from neutrinos and the φ' decaying to μ⁺μ^-. The analysis is performed using machine learning algorithms, over traditional methods, to maximize the signal sensitivity with an integrated luminosity of 3000 fb^-1. We make novel contributions by extending LHC constraints to φ' masses in the O(GeV)-O(100GeV) range where they have at least a 5σ sensitivity for all χ_u masses in the O(100GeV)-O(TeV) range. Further, we note the proposed methodology can be a key mode for discovery over a large mass range, including low masses, traditionally considered difficult due to experimental constraints.