Using Machine Learning techniques in phenomenological studies on flavour physics

Alda, J. (Universidad de Zaragoza) ; Guasch, J. ; Peñaranda, S. (Universidad de Zaragoza)

Resumen: An updated analysis of New Physics violating Lepton Flavour Universality, by using the Standard Model Effective Field Lagrangian with semileptonic dimension six operators at ¿ = 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.

Idioma: Inglés

DOI: 10.1007/JHEP07(2022)115

Año: 2022

Publicado en: Journal of High Energy Physics 2022, 7 (2022), 115 [43 pp.]
ISSN: 1126-6708

Factor impacto JCR: 5.4 (2022)

Categ. JCR: PHYSICS, PARTICLES & FIELDS rank: 6 / 29 = 0.207 (2022) - Q1 - T1

Factor impacto CITESCORE: 10.3 - Physics and Astronomy (Q1)

Factor impacto SCIMAGO: 0.859 - Nuclear and High Energy Physics (Q2)

Financiación: info:eu-repo/grantAgreement/ES/AEI/CEX2019-000918-M
Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2019-105614GB-C22
Financiación: info:eu-repo/grantAgreement/ES/DGIID-DGA/2015-E24-2
Financiación: info:eu-repo/grantAgreement/ES/MINECO-FEDER/PGC2018-095328-B-I00
Financiación: info:eu-repo/grantAgreement/ES/MINECO/FPA2015-65745-P

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Física Teórica (Dpto. Física Teórica)