Cargando…
Deep Regression of Muon Energy with a K-Nearest Neighbor Algorithm
Within the context of studies for novel measurement solutions for future particle physics experiments, we developed a performant kNN-based regressor to infer the energy of highly-relativistic muons from the pattern of their radiation losses in a dense and granular calorimeter. The regressor is based...
Autores principales: | Dorigo, Tommaso, Guglielmini, Sofia, Kieseler, Jan, Layer, Lukas, Strong, Giles C. |
---|---|
Lenguaje: | eng |
Publicado: |
2022
|
Materias: | |
Acceso en línea: | http://cds.cern.ch/record/2804577 |
Ejemplares similares
-
Calorimetric Measurement of Multi-TeV Muons via Deep Regression
por: Kieseler, Jan, et al.
Publicado: (2021) -
Muon Energy Measurement from Radiative Losses in a Calorimeter for a Collider Detector
por: Dorigo, Tommaso, et al.
Publicado: (2020) -
Reproducible Experiment Platform
por: Likhomanenko, Tatiana, et al.
Publicado: (2015) -
Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0
por: Lundberg, J., et al.
Publicado: (2009) -
Basics of Feature Selection and Statistical Learning for High Energy Physics
por: Vossen, Anselm
Publicado: (2008)