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. We propose a novel method to identify missing transverse energy vectors by comparing the detector response with Monte-Carlo simulated data. All calculated observables are provided as data sets for Deep Learning analysis, where a neural network is constructed, 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. Taking into account the effect of the three analysed topologies, we have found that the combined significance for the observation of new VLLs at the high-luminosity 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 and, in particular, providing important phenomenological information to constrain the energy scale at which new gauge symmetries emergent from the considered Grand Unification picture can be manifest.

在本文中，提出了一个受大统一原则启发的模型，该模型具有三代矢量样费米子，新的希格斯双峰和低阶中微子丰富的扇区。我们使用最先进的深度学习技术对该模型进行了首次现象学分析，重点是研究新的带电矢量样轻子（VLL）及其在CERN的大型强子对撞机（LHC）上的可能特征。在我们的数值分析中，我们考虑了矢量-玻色子融合和VLL对产生拓扑的信号事件，它们都涉及一个最终状态，该状态包含一对带不同风味的带电轻子和两个提供能量缺失的无菌中微子。我们还考虑了VLL单次生产的情况，其中除一对无菌中微子外，最终状态仅包含一个带电的轻子。我们提出了一种通过将检测器响应与蒙特卡洛模拟数据进行比较来识别缺失的横向能量矢量的新方法。所有计算得到的可观测值均作为数据集提供给深度学习分析，其中基于通过进化算法获得的结果构建神经网络，其目的是针对不同质量的VLL最大化准确性度量或Asimov重要性。考虑到这三种分析拓扑的影响，我们发现在高发光度LHC下观察新VLL的综合意义范围为5 其中基于通过进化算法获得的结果构建神经网络，其目的是针对不同质量的VLL最大化精度度量或Asimov有效性。考虑到这三种分析拓扑的影响，我们发现在高发光度LHC下观察新VLL的综合意义范围为5 其中基于通过进化算法获得的结果构建神经网络，其目的是针对不同质量的VLL最大化精度度量或Asimov有效性。考虑到这三种分析拓扑的影响，我们发现在高发光度LHC下观察新VLL的综合意义范围为5。7个σ，为1:1的质量。25 TeV的，一路多达28 σ如果VLL质量为200电子伏特。我们还显示，到LHC Run-III结束时，可以以8的置信度排除200 GeV VLL 。8个标准差。获得的结果表明，我们的模型可以在大型强子对撞机操作结束之前进行探测，并且特别是提供重要的现象学信息，以限制从所考虑的大统一图片中显现出新规范对称性的能级。