Probing Light Scalars and Vector-like Quarks at the High-Luminosity LHC

TL;DR

This paper explores the potential to discover light scalar bosons and vector-like quarks at the High-Luminosity LHC using a new phenomenological approach with machine learning, addressing challenging low-mass detection.

Contribution

It introduces a novel methodology employing machine learning for probing light scalars and vector-like quarks at the HL-LHC within an effective field theory framework.

Findings

Machine learning enhances signal sensitivity for new particles.

Proposed method covers a broad mass range, including low masses.

Analysis suggests feasible discovery potential at HL-LHC.

Abstract

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