ATLAS Collaboration(Aad, G. et al), Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Cardillo, F., Castillo Gimenez, V., et al. (2022). Search for new phenomena in three- or four-lepton events in pp collisions at root s=13 TeV with the ATLAS detector. Phys. Lett. B, 824, 136832–24pp.
Abstract: A search with minimal model dependence for physics beyond the Standard Model in events featuring three or four charged leptons (3l and 4l, l = e, mu) is presented. The analysis aims to be sensitive to a wide range of potential new-physics theories simultaneously. This analysis uses data from pp collisions delivered by the Large Hadron Collider at a centre-of-mass energy of root s = 13 TeV and recorded with the ATLAS detector, corresponding to the full Run 2 dataset of 139 fb(-1). The 3l and 4l phase space is divided into 22 event categories according to the number of leptons in the event, the missing transverse momentum, the invariant mass of the leptons, and the presence of leptons originating from a Z-boson candidate. These event categories are analysed independently for the presence of deviations from the Standard Model. No statistically significant deviations from the Standard Model predictions are observed. Upper limits for all signal regions are reported in terms of the visible cross-section.
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Kim, J. S., Lopez-Fogliani, D. E., Perez, A. D., & Ruiz de Austri, R. (2022). The new (g-2)(mu) and right-handed sneutrino dark matter. Nucl. Phys. B, 974, 115637–23pp.
Abstract: In this paper we investigate the (g – 2)(mu) discrepancy in the context of the R-parity conserving next-to minimal supersymmetric Standard Model plus right-handed neutrinos superfields. The model has the ability to reproduce neutrino physics data and includes the interesting possibility to have the right-handed sneutrino as the lightest supersymmetric particle and a viable dark matter candidate. Since right-handed sneutrinos are singlets, no new contributions for delta a(mu) with respect to the MSSM and NMSSM are present. However, the possibility to have the right-handed sneutrino as the lightest supersymmetric particle opens new ways to escape Large Hadron Collider and direct detection constraints. In particular, we find that dark matter masses within 10 less than or similar to m((upsilon) over tildeR) less than or similar to 600 GeV are fully compatible with current experimental constraints. Remarkably, not only spectra with light sleptons are needed, but we obtain solutions with m((mu) over tilde) greater than or similar to 600 GeV in the entire dark matter mass range that could be probed by new (g – 2)(mu) data in the near future. In addition, dark matter direct detection experiments will be able to explore a sizable portion of the allowed parameter space with mvR < 300 GeV, while indirect detection experiments will be able to probe a much smaller fraction within 200 less than or similar to m((nu)over tilde>R) less than or similar to 350 GeV.
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Coves, A., Maestre, H., Archiles, R., Andres, M. V., & Gimeno, B. (2022). Surface-Impedance Formulation for Hollow-Core Waveguides Based on Subwavelength Gratings. IEEE Access, 10, 18843–18854.
Abstract: A rigorous Surface Impedance (SI) formulation for planar waveguides is presented. This modal technique splits the modal analysis of the waveguide in two steps. First, we obtain the modes characteristic equations as a function of the SI and, second, we need to obtain the surface impedance values using either analytical or numerical methods. We validate the technique by comparison with well-known analytical cases: the parallel-plate waveguide with losses and the dielectric slab waveguide. Then, we analyze an optical hollow-core waveguide defined by two high-contrast subwavelength gratings validating our results by comparison with reported values. Finally, we show the potential of our formulation with the analysis of a THz hollow-core waveguide defined by two surface-relief subwavelength gratings, including material losses in our formulation.
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Khosa, C. K., & Sanz, V. (2022). On the Impact of the LHC Run 2 Data on General Composite Higgs Scenarios. Adv. High. Energy Phys., 2022, 8970837–13pp.
Abstract: We study the impact of Run 2 LHC data on general composite Higgs scenarios, where nonlinear effects, mixing with additional scalars, and new fermionic degrees of freedom could simultaneously contribute to the modification of Higgs properties. We obtain new experimental limits on the scale of compositeness, the mixing with singlets and doublets with the Higgs, and the mass and mixing angle of top-partners. We also show that for scenarios where new fermionic degrees of freedom are involved in electroweak symmetry breaking, there is an interesting interplay among Higgs coupling measurements, boosted Higgs properties, SMEFT global analyses, and direct searches for single and double production of vector-like quarks.
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Hirn, J., Garcia, J. E., Montesinos-Navarro, A., Sanchez-Martin, R., Sanz, V., & Verdu, M. (2022). A deep Generative Artificial Intelligence system to predict species coexistence patterns. Methods Ecol. Evol., 13, 1052–1061.
Abstract: Predicting coexistence patterns is a current challenge to understand diversity maintenance, especially in rich communities where these patterns' complexity is magnified through indirect interactions that prevent their approximation with classical experimental approaches. We explore cutting-edge Machine Learning techniques called Generative Artificial Intelligence (GenAI) to predict species coexistence patterns in vegetation patches, training generative adversarial networks (GAN) and variational AutoEncoders (VAE) that are then used to unravel some of the mechanisms behind community assemblage. The GAN accurately reproduces real patches' species composition and plant species' affinity to different soil types, and the VAE also reaches a high level of accuracy, above 99%. Using the artificially generated patches, we found that high-order interactions tend to suppress the positive effects of low-order interactions. Finally, by reconstructing successional trajectories, we could identify the pioneer species with larger potential to generate a high diversity of distinct patches in terms of species composition. Understanding the complexity of species coexistence patterns in diverse ecological communities requires new approaches beyond heuristic rules. Generative Artificial Intelligence can be a powerful tool to this end as it allows to overcome the inherent dimensionality of this challenge.
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