Eberhardt, O., Peñuelas, A., & Pich, A. (2021). Global fits in the Aligned Two-Higgs-Doublet model. J. High Energy Phys., 05(5), 005–37pp.
Abstract: We present the results of a global fit to the Aligned Two-Higgs Doublet Model, assuming that there are no new sources of CP violation beyond the quark mixing matrix. We use the most constraining flavour observables, electroweak precision measurements and the available data on Higgs signal strengths and collider searches for heavy scalars, together with the theoretical requirements of perturbativity and positivity of the scalar potential. The combination of all these constraints restricts the values of the scalar masses, the couplings of the scalar potential and the flavour-alignment parameters. The numerical fits have been performed using the open-source HEPfit package.
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Delhom, A., Miralles, V., & Peñuelas, A. (2020). Effective interactions in Ricci-Based Gravity below the non-metricity scale. Eur. Phys. J. C, 80(4), 340–14pp.
Abstract: We show how minimally-coupled matter fields of arbitrary spin, when coupled to Ricci-based gravity theories, develop non-trivial effective interactions that can be treated perturbatively only below a characteristic high-energy scale . We then use this interactions to set bounds on the high-energy scale that controls departures of Ricci-Based Gravity theories from General Relativity. Particularly, for Eddington-inspired Born-Infeld gravity we obtain the strong bound vertical bar kappa vertical bar<10(-26)m(5)kg(-1)s(-2).
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de Blas, J., Chowdhury, D., Ciuchini, M., Coutinho, A. M., Eberhardt, O., Fedele, M., et al. (2020). HEPfit: a code for the combination of indirect and direct constraints on high energy physics models. Eur. Phys. J. C, 80(5), 456–31pp.
Abstract: HEPfit is a flexible open-source tool which, given the Standard Model or any of its extensions, allows to (i) fit the model parameters to a given set of experimental observables; (ii) obtain predictions for observables. HEPfit can be used either in Monte Carlo mode, to perform a Bayesian Markov Chain Monte Carlo analysis of a given model, or as a library, to obtain predictions of observables for a given point in the parameter space of the model, allowing HEPfit to be used in any statistical framework. In the present version, around a thousand observables have been implemented in the Standard Model and in several new physics scenarios. In this paper, we describe the general structure of the code as well as models and observables implemented in the current release.
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