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dc.contributor.authorAlda, J.-
dc.contributor.authorGuasch Inglada, Jaume-
dc.contributor.authorPeñaranda Rivas, Siannah-
dc.description.abstractAn updated analysis of New Physics violating Lepton Flavour Universality, by using the Standard Model Effective Field Lagrangian with semileptonic dimension six operators at Λ = 1 TeV is presented. We perform a global fit, by discussing the relevance of the mixing in the first generation. We use for the first time in this context a Montecarlo analysis to extract the confidence intervals and correlations between observables. Our results show that machine learning, made jointly with the SHAP values, constitute a suitable strategy to use in this kind of analysis.-
dc.format.extent42 p.-
dc.publisherSpringer Verlag-
dc.relation.isformatofReproducció del document publicat a:
dc.relation.ispartofJournal of High Energy Physics, 2022, vol. 2022, num. 115, p. 1-42-
dc.rightscc-by (c) Alda, J. et al., 2022-
dc.sourceArticles publicats en revistes (Física Quàntica i Astrofísica)-
dc.subject.classificationFenomenologia (Física)-
dc.subject.classificationFísica de partícules-
dc.subject.classificationEquacions de Lagrange-
dc.subject.otherPhenomenological theory (Physics)-
dc.subject.otherParticle physics-
dc.subject.otherLagrange equations-
dc.titleUsing Machine Learning techniques in phenomenological studies on flavour physics-
Appears in Collections:Articles publicats en revistes (Física Quàntica i Astrofísica)
Articles publicats en revistes (Institut de Ciències del Cosmos (ICCUB))

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