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Abstract A model based on a$$U(1)_{T^3_R}$$ $U{\left(1\right)}_{T_R^3}$extension of the Standard Model can address the mass hierarchy between generations of fermions, explain thermal dark matter abundance, and the muon$$g - 2$$ $g-2$,$$R_{(D)}$$ $R_{\left(D\right)}$, and$$R_{(D^*)}$$ $R_{\left(D^{\ast }\right)}$anomalies. The model contains a light scalar boson$$\phi '$$ ${\varphi }^{\prime }$and a heavy vector-like quark$$\chi _\textrm{u}$$ ${\chi }_{\text{u}}$that can be probed at CERN’s large hadron collider (LHC). We perform a phenomenology study on the production of$$\phi '$$ ${\varphi }^{\prime }$and$${\chi }_u$$ ${\chi }_u$particles from proton–proton$$(\textrm{pp})$$ $\left(\text{pp}\right)$collisions at the LHC at$$\sqrt{s}=13.6$$ $\sqrt{s}=13.6$TeV, primarily through$$g{-g}$$ $g-g$and$$t{-\chi _\textrm{u}}$$ $t-{\chi }_{\text{u}}$fusion. We work under a simplified model approach and directly take the$$\chi _\textrm{u}$$ ${\chi }_{\text{u}}$and$$\phi '$$ ${\varphi }^{\prime }$masses as free parameters. We perform a phenomenological analysis considering$$\chi _\textrm{u}$$ ${\chi }_{\text{u}}$final states to b-quarks, muons, and neutrinos, and$$\phi '$$ ${\varphi }^{\prime }$decays to$$\mu ^+\mu ^-$$ ${\mu }^{+}{\mu }^{-}$. A machine learning algorithm is used to maximize the signal sensitivity, considering an integrated luminosity of 3000$$\text {fb}^{-1}$$ ${\text{fb}}^{-1}$. 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.