Phenomenology of vector-like leptons with Deep Learning at the Large Hadron Collider
Felipe F. Freitas, Jo\~ao Gon\c{c}alves, Ant\'onio P. Morais, Roman, Pasechnik

TL;DR
This paper presents a deep learning-based phenomenological analysis of vector-like leptons predicted by a Grand Unification-inspired model, demonstrating potential for discovery or exclusion at the LHC with high significance.
Contribution
It introduces a novel deep learning approach to analyze vector-like leptons signatures, providing the first such phenomenological study for this model at the LHC.
Findings
High significance detection potential for VLLs up to 1.25 TeV
200 GeV VLLs can be excluded with 8.8 sigma confidence
Deep learning enhances sensitivity in collider phenomenology
Abstract
In this paper, a model inspired by Grand Unification principles featuring three generations of vector-like fermions, new Higgs doublets and a rich neutrino sector at the low scale is presented. Using the state-of-the-art Deep Learning techniques we perform the first phenomenological analysis of this model focusing on the study of new charged vector-like leptons (VLLs) and their possible signatures at CERN's Large Hadron Collider (LHC). In our numerical analysis we consider signal events for vector-boson fusion and VLL pair production topologies, both involving a final state containing a pair of charged leptons of different flavor and two sterile neutrinos that provide a missing energy. We also consider the case of VLL single production where, in addition to a pair of sterile neutrinos, the final state contains only one charged lepton. All calculated observables are provided as data sets…
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