Phenomenology of vector-like leptons with Deep Learning at the LHC
Felipe F. Freitas, João Gonçalves, António P. Morais, Roman Pasechnik
… a model inspired by Grand Unification principles featuring 3 generations of vector-like fermions, new Higgs doublets and a rich neutrino sector at the low scale is presented. Using ... Deep Learning techniques we perform the first phenomenological analysis ... focusing on the study of new charged VLLs and their possible signatures ... In our numerical analysis we consider signal events for VBF 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. .. also consider the case of VLL single production... final state contains only one charged lepton. All calculated observables are provided as data sets for Deep Learning analysis... based on results obtained via an evolutive algorithm, whose objective is to maximise either the accuracy metric or the Asimov significance for different masses of the VLL. ...we have found that the combined significance for the observation of new VLLs at the HL-LHC can range from 5.7σ, for a mass of 1.25 TeV, all the way up to 28σ if the VLL mass is 200 GeV. We have also shown that by the end of the LHC Run-III a 200 GeV VLL can be excluded with a confidence of 8.8 standard deviations. The results obtained show that our model can be probed well before the end of the LHC operations ...