HL-LHC sensitivity to an ultraheavy $S_{uu}$ diquark in the $u\chi$ channel

TL;DR

This paper investigates the High-Luminosity LHC's ability to detect ultraheavy diquarks decaying into a quark and a vectorlike quark, using machine learning to enhance sensitivity in multijet final states.

Contribution

It introduces a new analysis strategy employing machine learning for detecting ultraheavy diquarks in four-jet topologies at the HL-LHC, extending previous six-jet studies.

Findings

Improved sensitivity to $S_{uu}$ in multi-TeV mass range.

Machine learning enhances discrimination of signal from background.

Provides discovery reach and exclusion limits for ultraheavy diquarks.

Abstract

We study the HL-LHC sensitivity to an ultraheavy diquark $S_{uu}$ produced in up-quark fusion and decaying as $S_{uu}\to u\chi$, $\chi\to Wb, Zt, h^0t$. For fully hadronic decays of the W, Z and top quark, this gives rise to multijet final states. Within the same model framework used previously for the $S_{uu}\to\chi\chi$ six-jet channel, we consider $S_{uu}$ masses in the multi-TeV range and vectorlike quark masses of order a few TeV, and simulate proton-proton collisions at $\sqrt s = 13.6$ TeV with integrated luminosities up to the HL-LHC target. The analysis strategy employs a machine-learning-based discriminant adapted from the six-jet study to the new four-jet topology, which we use to derive the corresponding discovery reaches and exclusion limits. We find that this topology improves the overall sensitivity to $S_{uu}$ in regions where the branching ratio $B(S_{uu}\to u\chi)$ is sizable and provides a complementary signature for studying ultraheavy diquarks at the HL-LHC.