Learning new physics efficiently with nonparametric methods

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We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. Based on the original p...

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Detalles Bibliográficos
Autores principales: Letizia, Marco, Losapio, Gianvito, Rando, Marco, Grosso, Gaia, Wulzer, Andrea, Pierini, Maurizio, Zanetti, Marco, Rosasco, Lorenzo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Regular Article - Experimental Physics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534824/
https://www.ncbi.nlm.nih.gov/pubmed/36212113
http://dx.doi.org/10.1140/epjc/s10052-022-10830-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534824/
https://www.ncbi.nlm.nih.gov/pubmed/36212113
http://dx.doi.org/10.1140/epjc/s10052-022-10830-y

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