Broad toplike vector quarks at LHC and HL-LHC

TL;DR

This paper investigates broad vector-like quarks with large decay widths at the LHC and HL-LHC, employing full propagator resummation and machine learning to improve detection prospects and set constraints.

Contribution

It introduces a comprehensive analysis of broad vector quarks using full propagator resummation and demonstrates the effectiveness of machine learning in collider searches for such resonances.

Findings

LHC constraints exclude masses below ~1.2 TeV for width/mass ratio of 0.1.

HL-LHC can discover broad vector quarks up to 1.6 TeV with 3 ab^{-1} data.

Machine learning enhances search efficiency over traditional methods.

Abstract

Top like vector quarks arising from underlying strong sectors are expected to have large decay widths pushing them beyond the narrow width approximation. In this paper we consider a broad colored vector quark that strongly couples to an exotic pseudoscalar. We use the full 1PI resummed propagator for the exotic quark to recast the present LHC constraints ruling out masses below $\sim1.2~(1.1)$ TeV for width to mass ratio of $0.1~(0.6)$. We utilize machine learning techniques that are demonstratively more efficient than traditional cut based searches to present the reach of HL-LHC on the parameter space of this broad resonance. We find that at $3~ab^{-1}$ the HL-LHC has a discovery potential up to $1.6$ TeV dominated by the pair production channel. We study the feasibility of using machine learning techniques to analyze the broad resonance peaks expected from these exotic quarks at collider experiments like the LHC.