IPAC2018 - List of Authors (Carlier, F.S.)

Paper	Title	Page
MOPMF032	Nonlinear Correction Strategies for the LHC Using Resonance Driving Terms	161
	F.S. Carlier, E.H. Maclean, T. Persson, R. Tomás CERN, Geneva, Switzerland
	The correction of nonlinearities in future colliders is critical to reach operational conditions and pose a significant challenge for commissioning schemes. Several approaches have been succesfully used in the LHC to correct sextupolar and octupolar sources in the LHC insertion regions. Measurements of resonance driving terms at top energy in the LHC have improved and now offer a new observable to calculate and validate nonlinear corrections. This paper reports on measurements of resonance driving terms in the LHC and the relevant strategies used for nonlinear corrections.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-MOPMF032
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MOPMF033	Probing the Forced Dynamic Aperture in the LHC at Top Energy Using AC Dipoles	165
SUSPF001	use link to see paper's listing under its alternate paper code
	F.S. Carlier, M. Giovannozzi, E.H. Maclean, T. Persson, R. Tomás CERN, Geneva, Switzerland
	Measurements of the dynamic aperture in colliders are a common method to ensure machine performance and offer an insight in the nonlinear content of the machine. Such direct measurements are very challenging for the LHC and High Luminosity LHC. Forced dynamic aperture has been demonstrated for the first time in the LHC at injection energy as a potential new observable to safely probe the nonlinear content of the machine. This paper presents the first measurements of forced dynamic aperture at top energy and discusses the proposed measurement schemes and challenges.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-MOPMF033
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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

Nonlinear Correction Strategies for the LHC Using Resonance Driving Terms

161

F.S. Carlier, E.H. Maclean, T. Persson, R. Tomás
CERN, Geneva, Switzerland

The correction of nonlinearities in future colliders is critical to reach operational conditions and pose a significant challenge for commissioning schemes. Several approaches have been succesfully used in the LHC to correct sextupolar and octupolar sources in the LHC insertion regions. Measurements of resonance driving terms at top energy in the LHC have improved and now offer a new observable to calculate and validate nonlinear corrections. This paper reports on measurements of resonance driving terms in the LHC and the relevant strategies used for nonlinear corrections.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-MOPMF032

Export •

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

MOPMF033

Probing the Forced Dynamic Aperture in the LHC at Top Energy Using AC Dipoles

165

SUSPF001

use link to see paper's listing under its alternate paper code

F.S. Carlier, M. Giovannozzi, E.H. Maclean, T. Persson, R. Tomás
CERN, Geneva, Switzerland

Measurements of the dynamic aperture in colliders are a common method to ensure machine performance and offer an insight in the nonlinear content of the machine. Such direct measurements are very challenging for the LHC and High Luminosity LHC. Forced dynamic aperture has been demonstrated for the first time in the LHC at injection energy as a potential new observable to safely probe the nonlinear content of the machine. This paper presents the first measurements of forced dynamic aperture at top energy and discusses the proposed measurement schemes and challenges.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-MOPMF033

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)