Search

Abstract The discovery potential of both singlet and doublet vector-like leptons (VLLs) at the Large Hadron Collider (LHC) as well as at the not-so-far future muon and electron machines is explored. The focus is on a single production channel for LHC direct searches while double production signatures are proposed for the leptonic colliders. A Deep Learning algorithm to determine the discovery (or exclusion) statistical significance at the LHC is employed. While doublet VLLs can be probed up to masses of 1 TeV, their singlet counterparts have very low cross sections and can hardly be tested beyond a few hundreds of GeV at the LHC. This motivates a physics-case analysis in the context of leptonic colliders where one obtains larger cross sections in VLL double production channels, allowing to probe higher mass regimes otherwise inaccessible even to the LHC high-luminosity upgrade.

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, up to a mass of 800 GeV in both the high-luminosity the Run-III phases of the LHC programme.

Abstract We propose a model with two Higgs doublets and several SU(2) scalar singlets with a global non-Abelian flavor symmetry Q 6 × Z 2 $$ {\mathcal{Q}}_6\times {\mathcal{Z}}_2 $$ . This discrete group accounts for the observed pattern of fermion masses and mixing angles after spontaneous symmetry breaking. In this scenario only the third generation of fermions get their masses as in the Standard Model (SM). The masses of the remaining fermions are generated through a seesaw-like mechanism. To that end, the matter content of the model is enlarged by introducing electrically charged vector-like fermions (VLFs), right handed Majorana neutrinos and several SM scalar singlets. Here we study the processes involving VLFs that are within the reach of the Large Hadron Collider (LHC). We perform collider studies for vector-like leptons (VLLs) and vector-like quarks (VLQs), focusing on double production channels for both cases, while for VLLs single production topologies are also included. Utilizing genetic algorithms for neural network optimization, we determine the statistical significance for a hypothetical discovery at future LHC runs. In particular, we show that we can not safely exclude VLLs for masses greater than 200 GeV. For VLQ’s in our model, we show that we can probe their masses up to 3.8 TeV, if we take only into account the high-luminosity phase of the LHC. Considering Run-III luminosities, we can also exclude VLQs for masses up to 3.4 TeV. We also show how the model with predicted VLL masses accommodates the muon anomalous magnetic moment.

A chiral-symmetric technicolor model successfully reconciles the tension between electroweak precision tests and traditional technicolor models. Focusing on its simplest realization preserving the conventional Higgs mechanism, we study its primordial gravitational wave signatures originating from first order phase transitions in the early Universe. We found that abundant phase transition patterns arise from a physically viable parameter space. Besides, we have also found gravitational wave signals possibly visible by future experiments, such as LISA, BBO and u-DECIGO. Our results stress the importance of gravitational wave detectors in exploring new physics complementary to ground colliders in the multi-messenger astronomy era.

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. 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.