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Title: Using Machine Learning techniques in phenomenological studies on flavour physics
Author: Alda, J.
Guasch Inglada, Jaume
Peñaranda Rivas, Siannah
Keywords: Fenomenologia (Física)
Física de partícules
Equacions de Lagrange
Phenomenological theory (Physics)
Particle physics
Lagrange equations
Issue Date: 19-Jul-2022
Publisher: Springer Verlag
Abstract: 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.
Note: Reproducció del document publicat a:
It is part of: Journal of High Energy Physics, 2022, vol. 2022, num. 115, p. 1-42
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ISSN: 1126-6708
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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