169368 20260225105429.0 doi 10.1007/s10773-026-06263-y sideral 148327 ART-2026-148327 eng Peñaranda, S. Universidad de Zaragoza (orcid)0000-0002-9408-4406 Flavour anomalies: a comparative analysis using a machine learning algorithm 2026 We present an analysis on flavour anomalies in semileptonic rare B-meson decays using an effective field theory approach and assuming that new physics affects only one generation in the interaction basis and non-universal mixing effects are generated by the rotation to the mass basis. A global fit to experimental data is performed, focusing on LFU ratios RD(∗) and RJ /ψ and branching ratios that exhibit tensions with Standard Model predictions on B → K(∗) νν¯ decays. In our analysis, we use a Machine Learning Montecarlo algorithm, a framework that emulates the highly non-Gaussian structure of the likelihood landscape with minimal training cost. This method enables the generation of high-resolution confidence regions and detailed correlation analyses. By comparing three different scenarios, we show that the one that introduces only mixing between the second and third quark generations and no mixing in the lepton sector, as well as independent coefficients for the singlet and triplet four fermion effective operators, provides the best fit to the experimental data. A comparison with previous results is performed. We highlight the key strengths of the Machine Learning framework in our analysis. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/ES/DGA-FSE/E21-17R info:eu-repo/grantAgreement/ES/MICINN/PGC2022-126078NB-C21 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Alda, J. Mir, A. Universidad de Zaragoza 2004 405 Universidad de Zaragoza Dpto. Física Teórica Área Física Teórica 65, 2 (2026), [22 pp.] Int. j. theor. phys. International journal of theoretical physics 0020-7748 1723184 http://zaguan.unizar.es/record/169368/files/texto_completo.pdf Versión publicada 1342645 http://zaguan.unizar.es/record/169368/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:169368 articulos driver 2026-02-24-14:47:41 ARTICLE