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Middeldorf-Wygas, M. M., Oldengott, I. M., Bödeker, D., & Schwarz, D. J. (2022). Cosmic QCD transition for large lepton flavor asymmetries. Phys. Rev. D, 105, 123533–10pp.
Abstract: We study the impact of large lepton flavor asymmetries on the cosmic QCD transition. Scenarios of unequal lepton flavor asymmetries are observationally almost unconstrained and therefore open up a whole new parameter space for the cosmic QCD transition. We find that for large asymmetries, the formation of a Bose-Einstein condensate of pions can occur and identify the corresponding parameter space. In the vicinity of the QCD transition scale, we express the pressure in terms of a Taylor expansion with respect to the complete set of chemical potentials. The Taylor coefficients rely on input from lattice QCD calculations from the literature. The domain of applicability of this method is discussed.
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Delhom, A., Nascimento, J. R., Olmo, G. J., Petrov, A. Y., & Porfirio, P. J. (2022). Radiative corrections in metric-affine bumblebee model. Phys. Lett. B, 826, 136932–9pp.
Abstract: We consider the metric-affine formulation of bumblebee gravity, derive the field equations, and show that the connection can be written as Levi-Civita of a disformally related metric in which the bumblebee field determines the disformal part. As a consequence, the bumblebee field gets coupled to all the other matter fields present in the theory, potentially leading to nontrivial phenomenological effects. To explore this issue we compute the weak-field limit and study the resulting effective theory. In this scenario, we couple scalar and spinorial matter to the effective metric which, besides the zeroth-order Minkowskian contribution, also has the vector field contributions of the bumblebee, and show that it is renormalizable at one-loop level. From our analysis it also follows that the non-metricity of this theory is determined by the gradient of the bumblebee field, and that it can acquire a vacuum expectation value due to the contribution of the bumblebee field.
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Folgado, M. G., & Sanz, V. (2022). Exploring the political pulse of a country using data science tools. J. Comput. Soc. Sci., 5, 987–1000.
Abstract: In this paper we illustrate the use of Data Science techniques to analyse complex human communication. In particular, we consider tweets from leaders of political parties as a dynamical proxy to political programmes and ideas. We also study the temporal evolution of their contents as a reaction to specific events. We analyse levels of positive and negative sentiment in the tweets using new tools adapted to social media. We also train a Fully-Connected Neural Network (FCNN) to recognise the political affiliation of a tweet. The FCNN is able to predict the origin of the tweet with a precision in the range of 71-75%, and the political leaning (left or right) with a precision of around 90%. This study is meant to be viewed as an example of how to use Twitter data and different types of Data Science tools for a political analysis.
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AbdusSalam, S. S. et al, & Eberhardt, O. (2022). Simple and statistically sound recommendations for analysing physical theories. Rep. Prog. Phys., 85(5), 052201–11pp.
Abstract: Physical theories that depend on many parameters or are tested against data from many different experiments pose unique challenges to statistical inference. Many models in particle physics, astrophysics and cosmology fall into one or both of these categories. These issues are often sidestepped with statistically unsound ad hoc methods, involving intersection of parameter intervals estimated by multiple experiments, and random or grid sampling of model parameters. Whilst these methods are easy to apply, they exhibit pathologies even in low-dimensional parameter spaces, and quickly become problematic to use and interpret in higher dimensions. In this article we give clear guidance for going beyond these procedures, suggesting where possible simple methods for performing statistically sound inference, and recommendations of readily-available software tools and standards that can assist in doing so. Our aim is to provide any physicists lacking comprehensive statistical training with recommendations for reaching correct scientific conclusions, with only a modest increase in analysis burden. Our examples can be reproduced with the code publicly available at Zenodo.
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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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