Cargando…
TrackML : a tracking Machine Learning challenge
The High-Luminosity LHC will see pileup levels reaching 200, which will greatly increase the complexity of the tracking component of the event reconstruction. To reach out to Computer Science specialists, a Tracking Machine Learning challenge (TrackML) was set up on Kaggle in 2018 by a team of ATLAS...
Autores principales: | Golling, Tobias, Amrouche, Sabrina, Kiehn, Moritz, Calafiura, Paolo, Farrell, Steven, Gray, Heather M, Estrade, Victor, Germain, Cécile, Gligorov, Vava, Guyon, Isabelle, Hushchyn, Mikhail, Ustyuzhanin, Andrey, Innocente, Vincenzo, Salzburger, Andreas, Moyse, Edward, Rousseau, David, Yilmaz, Yetkin, Vlimant, Jean-Roch |
---|---|
Lenguaje: | eng |
Publicado: |
SISSA
2019
|
Materias: | |
Acceso en línea: | https://dx.doi.org/10.22323/1.340.0159 http://cds.cern.ch/record/2704180 |
Ejemplares similares
-
The TrackML high-energy physics tracking challenge on Kaggle
por: Kiehn, Moritz, et al.
Publicado: (2019) -
TrackML: A High Energy Physics Particle Tracking Challenge
por: Calafiura, Polo, et al.
Publicado: (2018) -
The Tracking Machine Learning challenge : Throughput phase
por: Amrouche, Sabrina, et al.
Publicado: (2021) -
The Tracking Machine Learning challenge : Accuracy phase
por: Amrouche, Sabrina, et al.
Publicado: (2020) -
TrackML : The High Energy Physics Tracking Challenge
por: Rousseau, David
Publicado: (2018)