Using Machine Learning techniques in phenomenological studies in flavour physics
High Energy Physics - Phenomenology
2022-07-29 v2
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 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.
Cite
@article{arxiv.2109.07405,
title = {Using Machine Learning techniques in phenomenological studies in flavour physics},
author = {Jorge Alda and Jaume Guasch and Siannah Penaranda},
journal= {arXiv preprint arXiv:2109.07405},
year = {2022}
}
Comments
44 pages, 12 figures, 1 appendix. Version published on JHEP. Extended discussion and added a simplified leptoquark model, conclusions unchanged