IPAC2018 - List of Authors (Fol, E.)

Paper	Title	Page
MOPMF047	Transverse Coupling Measurements With High Intensity Beams Using Driven Oscillations	208
	T. Persson, G. Baud, X. Buffat, J.M. Coello de Portugal, E. Fol, K. Fuchsberger, M. Gabriel, M. Gąsior, M. Giovannozzi, G.H. Hemelsoet, M. Hostettler, M. Hruska, D. Jacquet, E.H. Maclean, L. Malina, J. Olexa, P.K. Skowroński, M. Solfaroli Camillocci, M.E. Söderén, R. Tomás, D. Valuch, A. Wegscheider, J. Wenninger CERN, Geneva, Switzerland
	Transverse coupling has been linked to instabilities and reduction in dynamic aperture and is hence a crucial parameter to control in the LHC. In this article we describe the development to use driven oscillations to measure the transverse coupling with high intensity beams. The method relies on the use of the transverse damper to drive an oscillation in a similar way as with an AC-dipole. The calculation of the coupling is based on the turn-by-turn data from all available BPMs gated for the excited bunch.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-MOPMF047
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WEPAF062	Machine Learning Methods for Optics Measurements and Corrections at LHC	1967
	E. Fol, F.S. Carlier, J.M. Coello de Portugal, A. Garcia-Tabares, R. Tomás CERN, Geneva, Switzerland
	The application of machine learning methods and concepts of artificial intelligence can be found in various industry and scientific branches. In Accelerator Physics the machine learning approach has not found a wide application yet. This paper is devoted to evaluation of machine learning methods aiming to improve the optics measurements and corrections at LHC. The main subjects of the study are devoted to recognition and analysis of faulty beam position monitors and prediction of quadrupole errors using clustering algorithms, decision trees and artificial neural networks. The results presented in this paper clearly show the suitability of machine learning methods for the optics control at LHC and the potential for further investigation on appropriate approaches.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-WEPAF062
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Transverse Coupling Measurements With High Intensity Beams Using Driven Oscillations

208

T. Persson, G. Baud, X. Buffat, J.M. Coello de Portugal, E. Fol, K. Fuchsberger, M. Gabriel, M. Gąsior, M. Giovannozzi, G.H. Hemelsoet, M. Hostettler, M. Hruska, D. Jacquet, E.H. Maclean, L. Malina, J. Olexa, P.K. Skowroński, M. Solfaroli Camillocci, M.E. Söderén, R. Tomás, D. Valuch, A. Wegscheider, J. Wenninger
CERN, Geneva, Switzerland

Transverse coupling has been linked to instabilities and reduction in dynamic aperture and is hence a crucial parameter to control in the LHC. In this article we describe the development to use driven oscillations to measure the transverse coupling with high intensity beams. The method relies on the use of the transverse damper to drive an oscillation in a similar way as with an AC-dipole. The calculation of the coupling is based on the turn-by-turn data from all available BPMs gated for the excited bunch.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-MOPMF047

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPAF062

Machine Learning Methods for Optics Measurements and Corrections at LHC

1967

E. Fol, F.S. Carlier, J.M. Coello de Portugal, A. Garcia-Tabares, R. Tomás
CERN, Geneva, Switzerland

The application of machine learning methods and concepts of artificial intelligence can be found in various industry and scientific branches. In Accelerator Physics the machine learning approach has not found a wide application yet. This paper is devoted to evaluation of machine learning methods aiming to improve the optics measurements and corrections at LHC. The main subjects of the study are devoted to recognition and analysis of faulty beam position monitors and prediction of quadrupole errors using clustering algorithms, decision trees and artificial neural networks. The results presented in this paper clearly show the suitability of machine learning methods for the optics control at LHC and the potential for further investigation on appropriate approaches.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-WEPAF062

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)