Phenomenology at the Large Hadron Collider with Deep Learning: the case of vector-like quarks decaying to light jets

TL;DR

This paper applies deep learning to analyze vector-like quark signatures at the LHC, combining jet images and kinematic data to improve detection sensitivity and exclude certain VLQ masses up to 800 GeV.

Contribution

It introduces a novel deep learning approach that combines jet images and tabular data for enhanced vector-like quark detection at the LHC.

Findings

Excluded VLQ masses up to 800 GeV at LHC

Combined jet images and kinematic data improves detection

Deep learning optimizes sensitivity for specific models

Abstract

In this work, we continue our exploration of TeV-scale vector-like fermion signatures inspired by a Grand Unification scenario based on the trinification gauge group. A particular focus is given to pair-production topologies of vector-like quarks (VLQs) at the LHC, in a multi-jet plus a charged lepton and a missing energy signature. We employ Deep Learning methods and techniques based in evolutive algorithms that optimize hyper-parameters in the neural network construction, whose objective is to maximise the Asimov estimate for distinct VLQ masses. In this article, we consider the implications of an innovative approach by simultaneously combining detector images (also known as jet images) and tabular data containing kinematic information from the final states. With this technique we are able to exclude VLQs, that are specific for the considered model, to up a mass of 800 GeV in both the high-luminosity the Run-III phases of the LHC programme.