IBIC2018 - List of Authors (Tomas, R.)

Paper	Title	Page
TUOA02	Application of Machine Learning to Beam Diagnostics	169
	E. Fol, J.M. Coello de Portugal, R. Tomás CERN, Geneva, Switzerland
	Machine learning techniques are used in various scientific and industry fields as a powerful tool for data analysis and automatization. The presentation is devoted to exploration of relevant machine learning methods for beam diagnostics. The target is to provide an insight into modern machine learning techniques, which can be applied to improve current beam diagnostics and general applications in accelerators. Possible concepts for future applications are also presented.
	Slides TUOA02 [2.497 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IBIC2018-TUOA02
About •	paper received ※ 04 September 2018 paper accepted ※ 10 September 2018 issue date ※ 29 January 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUOB02	Optics Measurements in Storage Rings: Simultaneous 3-Dimensional Beam Excitation and Novel Harmonic Analysis	177
	L. Malina, J.M. Coello de Portugal, J. Dilly, P.K. Skowroński, R. Tomás CERN, Geneva, Switzerland
	Optics measurements in storage rings employ turn-by-turn data of transversely excited beams. Chromatic parameters need measurements to be repeated at different beam energies, which is time-consuming. We present an optics measurement method based on adiabatic simultaneous 3-dimensional beam excitation, where no repetition at different energies is needed. In the LHC, the method has been successfully demonstrated utilising AC-dipoles combined with RF frequency modulation. It allows measuring the linear optics parameters and chromatic properties at the same time without resolution deterioration. We also present a new accurate harmonic analysis algorithm that exploits the noise cleaning based on singular value decomposition to compress the input data. In the LHC, this sped up harmonic analysis by a factor up to 300. These methods are becoming a "push the button" operational tool to measure the optics.
	Slides TUOB02 [1.117 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IBIC2018-TUOB02
About •	paper received ※ 04 September 2018 paper accepted ※ 10 September 2018 issue date ※ 29 January 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Application of Machine Learning to Beam Diagnostics

169

E. Fol, J.M. Coello de Portugal, R. Tomás
CERN, Geneva, Switzerland

Machine learning techniques are used in various scientific and industry fields as a powerful tool for data analysis and automatization. The presentation is devoted to exploration of relevant machine learning methods for beam diagnostics. The target is to provide an insight into modern machine learning techniques, which can be applied to improve current beam diagnostics and general applications in accelerators. Possible concepts for future applications are also presented.

Slides TUOA02 [2.497 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IBIC2018-TUOA02

About •

paper received ※ 04 September 2018 paper accepted ※ 10 September 2018 issue date ※ 29 January 2019

Export •

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

TUOB02

Optics Measurements in Storage Rings: Simultaneous 3-Dimensional Beam Excitation and Novel Harmonic Analysis

177

L. Malina, J.M. Coello de Portugal, J. Dilly, P.K. Skowroński, R. Tomás
CERN, Geneva, Switzerland

Optics measurements in storage rings employ turn-by-turn data of transversely excited beams. Chromatic parameters need measurements to be repeated at different beam energies, which is time-consuming. We present an optics measurement method based on adiabatic simultaneous 3-dimensional beam excitation, where no repetition at different energies is needed. In the LHC, the method has been successfully demonstrated utilising AC-dipoles combined with RF frequency modulation. It allows measuring the linear optics parameters and chromatic properties at the same time without resolution deterioration. We also present a new accurate harmonic analysis algorithm that exploits the noise cleaning based on singular value decomposition to compress the input data. In the LHC, this sped up harmonic analysis by a factor up to 300. These methods are becoming a "push the button" operational tool to measure the optics.

Slides TUOB02 [1.117 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IBIC2018-TUOB02

About •

paper received ※ 04 September 2018 paper accepted ※ 10 September 2018 issue date ※ 29 January 2019

Export •

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