2026-04-17T04:07:35Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/1896262025-11-21T05:47:15Zcom_2072_1057col_2072_478823col_2072_478904col_2072_478917

Using Machine Learning techniques in phenomenological studies on flavour physics Alda, J. Guasch Inglada, Jaume Peñaranda Rivas, Siannah Fenomenologia (Física) Física de partícules Equacions de Lagrange Phenomenological theory (Physics) Particle physics Lagrange equations An 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. 2022-10-05T14:48:44Z 2022-10-05T14:48:44Z 2022-07-19 2022-10-05T14:48:45Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Reproducció del document publicat a: https://doi.org/10.1007/JHEP07(2022)115 Journal of High Energy Physics, 2022, vol. 2022, num. 115, p. 1-42 https://doi.org/10.1007/JHEP07(2022)115 cc-by (c) Alda, J. et al., 2022 https://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Springer Verlag Articles publicats en revistes (Física Quàntica i Astrofísica)