  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.
