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A Neural-Network-defined Gaussian Mixture Model for particle identification applied to the LHCb fixed-target programme

Particle identification in large high-energy physics experiments typically relies on classifiers obtained by combining many experimental observables. Predicting the probability density function (pdf) of such classifiers in the multivariate space covering the relevant experimental features is usually...

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Detalles Bibliográficos
Autores principales: Graziani, Giacomo, Anderlini, Lucio, Mariani, Saverio, Franzoso, Edoardo, Pappalardo, Luciano Libero, di Nezza, Pasquale
Lenguaje:eng
Publicado: 2021
Materias:
Acceso en línea:https://dx.doi.org/10.1088/1748-0221/17/02/P02018
http://cds.cern.ch/record/2788509