Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/189626
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: https://doi.org/10.1007/JHEP07(2022)115 |
It is part of: | Journal of High Energy Physics, 2022, vol. 2022, num. 115, p. 1-42 |
URI: | http://hdl.handle.net/2445/189626 |
Related resource: | https://doi.org/10.1007/JHEP07(2022)115 |
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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File | Description | Size | Format | |
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724267.pdf | 3.36 MB | Adobe PDF | View/Open |
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