Using Machine Learning techniques in phenomenological studies in flavour physics

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 $\Lambda =1\mathrm{TeV}$ 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.

44 pages, 12 figures, 1 appendix. Version published on JHEP. Extended discussion and added a simplified leptoquark model, conclusions unchanged

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Cited by in corpus (3)

• Fast multilabel classification of HEP constraints with deep learning
• Flavour Anomalies: A comparative analysis using a machine learning algorithm
• Lecture notes on Machine Learning applications for global fits