Beam Measurements and Machine Learning at the CERN Large Hadron Collider
P. Arpaia, G. Azzopardi, F. Blanc, X. Buffat, L. Coyle, E. Fol, F., Giordano, M. Giovannozzi, T. Pieloni, R. Prevete, S. Redaelli, B. Salvachua,, B. Salvant, M. Schenk, M. Solfaroli Camillocci, R. Tom\'as, G. Valentino,, F.F. Van der Veken, J. Wenninger
TL;DR
This paper reviews recent machine learning applications to beam measurements at CERN's Large Hadron Collider, highlighting advancements in measurement techniques and data analysis methods.
Contribution
It provides a comprehensive overview of how machine learning is applied to beam measurement challenges at CERN, offering insights into recent developments.
Findings
Enhanced measurement accuracy through machine learning techniques
Improved data analysis efficiency at the LHC
Potential for future automation of beam diagnostics
Abstract
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.
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Taxonomy
TopicsParticle Detector Development and Performance · Particle physics theoretical and experimental studies · Superconducting Materials and Applications