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Han, C., Lopez-Ibañez, M. L., Melis, A., Vives, O., Wu, L., & Yang, J. M. (2020). LFV and (g-2) in non-universal SUSY models with light higgsinos. J. High Energy Phys., 05(5), 102–32pp.
Abstract: We consider a supersymmetric type-I seesaw framework with non-universal scalar masses at the GUT scale to explain the long-standing discrepancy of the anomalous magnetic moment of the muon. We find that it is difficult to accommodate the muon g-2 while keeping charged-lepton flavor violating processes under control for the conventional SO(10)-based relation between the up sector and neutrino sector. However, such tension can be relaxed by adding a Georgi-Jarlskog factor for the Yukawa matrices, which requires a non-trivial GUT-based model. In this model, we find that both observables are compatible for small mixings, CKM-like, in the neutrino Dirac Yukawa matrix.
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Calibbi, L., Lopez-Ibañez, M. L., Melis, A., & Vives, O. (2020). Muon and electron g – 2 and lepton masses in flavor models. J. High Energy Phys., 06(6), 087–23pp.
Abstract: The stringent experimental bound on μ-> e gamma is compatible with a simultaneous and sizable new physics contribution to the electron and muon anomalous magnetic moments (g – 2)(l) (l = e, mu), only if we assume a non-trivial flavor structure of the dipole operator coefficients. We propose a mechanism in which the realization of the (g – 2)(l) correction is manifestly related to the mass generation through a flavor symmetry. A radiative flavon correction to the fermion mass gives a contribution to the anomalous magnetic moment. In this framework, we introduce a chiral enhancement from a non-trivial O(1) quartic coupling of the scalar potential. We show that the muon and electron anomalies can be simultaneously explained in a vast region of the parameter space with predicted vector-like mediators of masses as large as M chi is an element of [0.6, 2.5] TeV.
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Sanchis-Lozano, M. A., & Sarkisyan-Grinbaum, E. K. (2018). Searching for new physics with three-particle correlations in pp collisions at the LHC. Phys. Lett. B, 781, 505–509.
Abstract: New phenomena involving pseudorapidity and azimuthal correlations among final-state particles in pp collisions at the LHC can hint at the existence of hidden sectors beyond the Standard Model. In this paper we rely on a correlated-cluster picture of multiparticle production, which was shown to account for the ridge effect, to assess the effect of a hidden sector on three-particle correlations concluding that there is a potential signature of new physics that can be directly tested by experiments using well-known techniques.
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Real, D., & Calvo, D. (2022). Production requirements and functional tests of the KM3NeT Digital Optical Module Power Board. Nucl. Instrum. Methods Phys. Res. A, 1042, 167426–3pp.
Abstract: The KM3NeT research facility is being built in the Mediterranean Sea. It consists of matrices of optical detectors, the so-called Digital Optical Module. Each of this elementary detector holds a set of 31 small-area photomultipliers, which detect the Cherenkov light generated by secondary particles produced in neutrino interactions. It includes also the acquisition electronics and the power board which supplies both, the acquisition electronics and the photomultipliers. The production of electronics boards needs to have a high quality and reliability level as it is going to be deployed for more than ten years without any maintenance possible. This work presents the requirements and the qualification tests being implemented in order to increase the reliability of the Power Board of the acquisition electronics of KM3NeT during the mass production. At the moment, more than one thousand board have been produced. Results on the production of the boards, including the production yield is presented. From the already produced boards, more than 350 have been already deployed and are operative in the detectors.
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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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