Machine learning for beam dynamics studies at the CERN Large Hadron Collider
- Arpaia P.
- Azzopardi G.
- Blanc F.
- Bregliozzi G.
- Buffat X.
- Coyle L.
- Fol E.
- Giordano F.
- Giovannozzi M.
- Pieloni T.
- Prevete R.
- Redaelli S.
- Salvachua B.
- Salvant B.
- Schenk M.
- Camillocci M. S.
- Tomas R.
- Valentino G.
- Van der Veken F. F.
- Wenninger J.
DOIAbstract
Machine learning entails a broad range of techniques that have been widely used in Science and Engineering since decades. High-energy physics has also profited from the power of these tools for advanced analysis of colliders data. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisatio...
Add to ORKG