Cargando…
Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors
The physics potential of massive liquid argon TPCs in the low-energy regime is still to be fully reaped because few-hits events encode information that can hardly be exploited by conventional classification algorithms. Machine learning (ML) techniques give their best in these types of classification...
Autores principales: | Biassoni, Matteo, Giachero, Andrea, Grossi, Michele, Guffanti, Daniele, Labranca, Danilo, Moretti, Roberto, Rossi, Marco, Terranova, Francesco, Vallecorsa, Sofia |
---|---|
Lenguaje: | eng |
Publicado: |
2023
|
Materias: | |
Acceso en línea: | http://cds.cern.ch/record/2861063 |
Ejemplares similares
-
Towards Optimal Compression: Joint Pruning and Quantization
por: Zandonati, Ben, et al.
Publicado: (2023) -
Technical Report of Participation in Higgs Boson Machine Learning Challenge
por: Ahmad, S. Raza
Publicado: (2015) -
Variational Dropout Sparsification for Particle Identification speed-up
por: Ryzhikov, Artem, et al.
Publicado: (2020) -
Hyperparameter optimization, quantum-assisted model performance prediction, and benchmarking of AI-based High Energy Physics workloads using HPC
por: Wulff, Eric, et al.
Publicado: (2023) -
Generative Models for Fast Calorimeter Simulation: the LHCb case
por: Chekalina, Viktoria, et al.
Publicado: (2019)