Confronting Vector-Like Quark Models with LHC Searches

TL;DR

VLQBounds is a Python framework that enables efficient testing of Vector-Like Quark models against LHC experimental limits, facilitating reinterpretation and phenomenological studies.

Contribution

It introduces a modular, data-driven tool that automates the comparison of VLQ model predictions with LHC exclusion limits across multiple parameterizations.

Findings

Automates exclusion testing for VLQ models using LHC data.

Supports various parameterizations like mass-mixing and mass-width.

Enables fast scans and validation of experimental limits.

Abstract

We present VLQBounds, a public, data-driven Python framework for testing Vector-Like Quark (VLQ) scenarios against Large Hadron Collider (LHC) exclusion limits from ATLAS and CMS. The framework incorporates public results on both pair and single VLQ production and supports the main parameterisations used in experimental interpretations, including mass-mixing, mass-coupling, and mass-width representations. For each parameter point, the predicted cross-section or effective coupling is compared channel by channel to the corresponding observed and expected experimental limits through interpolation over machine-readable grids. The most sensitive analysis is automatically identified and a 95\% Confidence-Level exclusion verdict is returned, together with the observed and expected sensitivity ratios and the metadata needed for reproducible reinterpretation. The modular structure of VLQBounds makes it suitable for fast phenomenological scans, validation of public limits, and future extensions to new collider searches and non-minimal VLQ decay patterns.