How to Use Machine Learning to Improve the Discrimination between Signal and Background at Particle Colliders
The popularity of Machine Learning (ML) has been increasing in recent decades in almost every area, with the commercial and scientific fields being the most notorious ones. In particle physics, ML has been proven a useful resource to make the most of projects such as the Large Hadron Collider (LHC)....
Guardado en:
Autores principales: | Xabier Cid Vidal, Lorena Dieste Maroñas, Álvaro Dosil Suárez |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/98c59779bc1448c9890aad3e86c9de61 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider
por: Anna Stakia, et al.
Publicado: (2021) -
A High-Granularity Timing Detector for the Phase-II upgrade of the ATLAS Calorimeter system: detector concept, description and R&D and beam test results
por: Imam H.
Publicado: (2021) -
Exploration of Extended Higgs Sectors with Run-2 Proton–Proton Collision Data at the LHC
por: Arnaud Ferrari, et al.
Publicado: (2021) -
Neutron spin structure from e-3He scattering with double spectator tagging at the electron-ion collider
por: I. Friščić, et al.
Publicado: (2021) -
Contextual Advantage for State Discrimination
por: David Schmid, et al.
Publicado: (2018)