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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...
| Autores principales: | , , , , , |
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| Lenguaje: | eng |
| Publicado: |
2021
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1088/1748-0221/17/02/P02018 http://cds.cern.ch/record/2788509 |