Beam Measurements and Machine Learning at the CERN Large Hadron Collider
| Autor: | Stefano Redaelli, Benoit Salvant, F. Blanc, Roberto Prevete, Matteo Solfaroli Camillocci, Jorg Wenninger, F. Giordano, Massimo Giovannozzi, Tatiana Pieloni, Elena Fol, Loic Coyle, Gabriella Azzopardi, Xavier Buffat, Pasquale Arpaia, Rogelio Tomás, Gianluca Valentino, Michael Schenk, Frederik Van Der Veken, Belen Salvachua |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Accelerator Physics (physics.acc-ph)
Physics Particle physics Large Hadron Collider Physics::Instrumentation and Detectors Physics beyond the Standard Model FOS: Physical sciences Particle accelerator Computer Science::Digital Libraries Accelerators and Storage Rings Standard Model law.invention Atmospheric measurements law Physics::Accelerator Physics Physics - Accelerator Physics High Energy Physics::Experiment Electrical and Electronic Engineering Particle physics experiments lhc Instrumentation Beam (structure) physics.acc-ph |
| Popis: | Particle accelerators are among the most complex instruments conceived by physicists for the exploration of the fundamental laws of nature. Of relevance for particle physics are the high-energy colliders, such as the CERN Large Hadron Collider (LHC), which hosts particle physics experiments that are probing the Standard Model predictions and looking for signs of physics beyond the standard model. This paper presents a review of the recent Machine Learning activities carried out on beam measurements performed at the CERN Large Hadron Collider. This paper has been accepted for publication in IEEE Instrumentation and Measurement Magazine and in the published version no abstract is provided. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|