Borsato, M. et al, Zurita, J., Henry, L., Jashal, B. K., & Oyanguren, A. (2022). Unleashing the full power of LHCb to probe stealth new physics. Rep. Prog. Phys., 85(2), 024201–45pp.
Abstract: In this paper, we describe the potential of the LHCb experiment to detect stealth physics. This refers to dynamics beyond the standard model that would elude searches that focus on energetic objects or precision measurements of known processes. Stealth signatures include long-lived particles and light resonances that are produced very rarely or together with overwhelming backgrounds. We will discuss why LHCb is equipped to discover this kind of physics at the Large Hadron Collider and provide examples of well-motivated theoretical models that can be probed with great detail at the experiment.
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Alvarez, A., Cepedello, R., Hirsch, M., & Porod, W. (2022). Temperature effects on the Z(2) symmetry breaking in the scotogenic model. Phys. Rev. D, 105(3), 035013–8pp.
Abstract: It is well known that the scotogenic model for neutrino mass generation can explain correctly the relic abundance of cold dark matter. There have been claims in the literature that an important part of the parameter space of the simplest scotogentic model can be constrained by the requirement that no Z(2)-breaking must occur in the early universe. Here we show that this requirement does not give any constraints on the underlying parameter space at least in those parts, where we can trust perturbation theory. To demonstrate this, we have taken into account the proper decoupling of heavy degrees of freedom in both the thermal potential and in the RGE evolution.
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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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Coogan, A., Bertone, G., Gaggero, D., Kavanagh, B. J., & Nichols, D. A. (2022). Measuring the dark matter environments of black hole binaries with gravitational waves. Phys. Rev. D, 105(4), 043009–22pp.
Abstract: Large dark matter overdensities can form around black holes of astrophysical and primordial origin as they form and grow. This “dark dress” inevitably affects the dynamical evolution of binary systems and induces a dephasing in the gravitational waveform that can be probed with future interferometers. In this paper, we introduce a new analytical model to rapidly compute gravitational waveforms in the presence of an evolving dark matter distribution. We then present a Bayesian analysis determining when dressed black hole binaries can be distinguished from GR-in-vacuum ones and how well their parameters can be measured, along with how close they must be to be detectable by the planned Laser Interferometer Space Antenna (LISA). We show that LISA can definitively distinguish dark dresses from standard binaries and characterize the dark matter environments around astrophysical and primordial black holes for a wide range of model parameters. Our approach can be generalized to assess the prospects for detecting, classifying, and characterizing other environmental effects in gravitational wave physics.
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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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