Search

We propose a model with two Higgs doublets and several $SU(2)$ scalar singlets with a global non-Abelian flavor symmetry $\mathcal{Q}_6\times\mathcal{Z}_2$. 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 $200~\mathrm{GeV}$. 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 $3.4~\mathrm{TeV}$. We also show how the model with predicted VLL masses accommodates the muon anomalous magnetic moment., Comment: 32 pages,16 figures. Additional references and discussion. Matches version to be published in JHEP

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 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 $5.7\sigma$, for a mass of $1.25~\mathrm{TeV}$, all the way up to $28\sigma$ if the VLL mass is $200~\mathrm{GeV}$. We have also shown that by the end of the LHC Run-III a $200~\mathrm{GeV}$ 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., Comment: 60 pages, 31 figures, 15 tables

We construct families, and concrete examples, of simple extensions of the Standard Model that can yield ultralight {real or} complex vectors or scalars with potential astrophysical relevance. Specifically, the mass range for these putative fundamental bosons ($\sim 10^{-10}-10^{-20}$ eV) would lead dynamically to both new non-black hole compact objects (bosonic stars) and new non-Kerr black holes, with masses of $\sim M_\odot$ to $\sim 10^{10} M_\odot$, corresponding to the mass range of astrophysical black hole candidates (from stellar mass to supermassive). For each model, we study the properties of the mass spectrum and interactions after spontaneous symmetry breaking, discuss its theoretical viability and caveats, as well as some of its potential and most relevant phenomenological implications {linking them to the} physics of compact objects., Comment: 27 pages, 2 figures

Inverse seesaw is a genuine TeV scale seesaw mechanism. In it active neutrinos with masses at eV scale requires lepton number be explicitly violated at keV scale and the existence of new physics, in the form of heavy neutrinos, at TeV scale. Therefore it is a phenomenologically viable seesaw mechanism since its signature may be probed at the LHC. Moreover it is successfully embedded into gauge extensions of the standard model as the 3-3-1 model with the right-handed neutrinos. In this work we revisit the implementation of this mechanism into the 3-3-1 model and employ deep learning analysis to probe such setting at the LHC and, as main result, we have that if its signature is not detected in the next LHC running with energy of 14 TeVs, then, the vector boson $Z^{\prime}$ of the 3-3-1 model must be heavier than 4 TeVs. Inverse seesaw is a genuine TeV scale seesaw mechanism. In it active neutrinos with masses at eV scale requires lepton number be explicitly violated at keV scale and the existence of new physics, in the form of heavy neutrinos, at TeV scale. Therefore it is a phenomenologically viable seesaw mechanism since its signature may be probed at the LHC. Moreover it is successfully embedded into gauge extensions of the standard model as the 3-3-1 model with the right-handed neutrinos. In this work we revisit the implementation of this mechanism into the 3-3-1 model and employ deep learning analysis to probe such setting at the LHC and, as main result, we have that if its signature is not detected in the next LHC running with energy of 14 TeVs, then, the vector boson Z′ of the 3-3-1 model must be heavier than 4 TeVs.

The data that support the findings of this study are openly available at the following URL/DOI: https://arxiv.org/abs/2011.10425., We explore machine learning methods to detect gravitational waves (GW) from binary black hole (BBH) mergers using deep learning (DL) algorithms. The DL networks are trained with gravitational waveforms obtained from BBH mergers with component masses randomly sampled in the range from 5 to 100 solar masses and luminosity distances from 100 Mpc to, at least, 2000 Mpc. The GW signal waveforms are injected in public data from the O2 run of the Advanced LIGO and Advanced Virgo detectors, in time windows that do not coincide with those of known detected signals. We demonstrate that DL algorithms, trained with GW signal waveforms at distances of 2000 Mpc, still show high accuracy when detecting closer signals, within the ranges considered in our analysis. Moreover, by combining the results of the three-detector network in a unique RGB image, the single detector performance is improved by as much as 70%. Furthermore, we train a regression network to perform parameter inference on BBH spectrogram data and apply this network to the events from the GWTC-1 and GWTC-2 catalogs. Without significant optimization of our algorithms we obtain results that are mostly consistent with published results by the LIGO-Virgo Collaboration. In particular, our predictions for the chirp mass are compatible (up to 3 sigma) with the official values for 90% of events. From these results we conclude that the combination of computer vision techniques and deep-learning methods put forward in this work is a worthy addition to the GW astronomer's toolbox., We thank Nicolas Sanchis-Gual for very fruitful discussions that allowed to setup the research team involved in this investigation. This work was supported by the Spanish Agencia Estatal de Investigacion (PGC2018-095984-B-I00), by the Generalitat Valenciana (PROMETEO/2019/071), by the European Union's Horizon 2020 research and innovation (RISE) programme (H2020-MSCA-RISE-2017 Grant No. FunFiCO-777740) and by the Portuguese Foundation for Science and Technology (FCT), project CERN/FIS-PAR/0029/2019. APM and FFF are supported by the FCT project PTDC/FIS-PAR/31000/2017 and by the Center for Research and Development in Mathematics and Applications (CIDMA) through FCT, references UIDB/04106/2020 and UIDP/04106/2020. APM is also supported by the projects CERN/FIS-PAR/0027/2019, CERN/FISPAR/0002/2017 and by national funds (OE), through FCT, IP, in the scope of the framework contract foreseen in the numbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017, of July 19., info:eu-repo/semantics/publishedVersion

In this paper we study the use of machine learning techniques to exploit kinematic information in VH, the production of a Higgs in association with a massive vector boson. We parametrize the effect of new physics in terms of the standard model effective field theory (SMEFT) framework. We find that the use of a shallow neural network allows us to dramatically increase the sensitivity to deviations in VH respect to previous estimates. We also discuss the relation between the usual measures of performance in machine learning, such as area under the curve or accuracy, with the more adept measure of Asimov significance. This relation is particularly relevant when parametrizing systematic uncertainties. Our results show the potential of incorporating machine learning techniques to the SMEFT studies using the current datasets.

After the LHC is turning on and accumulating more data, the TeV scale seesaw mechanisms for small neutrino masses in the form of inverse seesaw mechanisms are gaining more and more attention once they provide neutrino masses at sub-eV scale and can be probed at the LHC. Here we restrict our investigation to the inverse type II seesaw case and implement it into the framework of the 3-3-1 model with right-handed neutrinos. As interesting result, the mechanism provides small masses to both the standard neutrinos as well as to the right-handed ones. Its best signature are the doubly charged scalars which are sextet by the 3-3-1 symmetry. We investigate their production at the LHC through the process $\sigma (p\,p \rightarrow Z^*, \gamma^* ,Z^{\prime} \rightarrow \Delta^{++}\,\Delta^{--})$ and their signal through four leptons final state decay channel., Comment: 21 pages, 10 figures, minor changes, reference updated

We present the most general sum rules reflecting the cancellation of ultraviolet divergences in the Higgs potential in weakly-coupled, natural extensions of the Standard Model. There is a separate sum rule for the cancellation of the quadratic and logarithmic divergences, and their forms depend on whether the divergences are canceled by same-spin or opposite-spin partners. These sum rules can be applied to mass eigenstates and conveniently used for direct collider tests of naturalness. We study in detail the feasibility of testing these sum rules in the top sector at a future $100\TeV$ proton collider within two benchmark models, the Little Higgs (LH) and the Maximally Symmetric Composite Higgs (MSCH). We show how the two ingredients of the sum rules, the top partner masses and their Yukawa couplings to the Higgs, can be measured with sufficient accuracy to provide a highly non-trivial quantitative test of the sum rules. In particular, we study observables sensitive to the sign of the top partner Yukawa, which is crucial for verifying the sum rules but is notoriously difficult to measure. We demonstrate that in the benchmark models under study, a statistically significant discrimination between the two possible signs of each Yukawa will be feasible with a 30 ab$^{-1}$ data set at $100\TeV$., 26 pages +9 appendices

The advent of the LHC, and the proposal of building future colliders as the ILC, both programmed to explore new physics at the TeV scale, justifies the recent interest in studying all kind of seesaw mechanisms whose signature lies on such energy scale. The natural candidate for this kind of seesaw mechanism is the inverse one. The conventional inverse seesaw mechanism is implemented in an arrangement involving six new heavy neutrinos in addition to the three standard ones. In this paper we develop the inverse seesaw mechanism based on Higgs triplet model and probe its signature at the LHC and ILC. We argue that the conjoint analysis of the LHC together with the ILC may confirm the mechanism and, perhaps, infer the hierarchy of the neutrino masses., Comment: 24 pages, 22 figures