IBIC2018 - List of Keywords (network)

Paper	Title	Other Keywords	Page
TUOA02	Application of Machine Learning to Beam Diagnostics	optics, diagnostics, controls, simulation	169
	E. Fol, J.M. Coello de Portugal, R. Tomás CERN, Meyrin, 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)

WEPC15	Machine Learning Applied to Predict Transverse Oscillation at SSRF	diagnostics, SRF, injection, storage-ring	512
	B. Gao, J. Chen, Y.B. Leng, Y.M. Zhou SINAP, Shanghai, People’s Republic of China
	A fast beam size diagnostic system has been developed at SSRF (Shanghai Synchrotron Radiation Facility) storage ring for turn-by-turn and bunch-by-bunch beam transverse oscillation study. This system is based on visible synchrotron radiation direct imaging system. Currently, this system already has good experimental results. However, this system still has some limitations, the resolution is subject to the point spread function and the speed of online data processing is limited by the complex algorithm. We present a technique that applied machine learning tools to predict transverse oscillation.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IBIC2018-WEPC15
About •	paper received ※ 05 September 2018 paper accepted ※ 13 September 2018 issue date ※ 29 January 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Other Keywords

Page

TUOA02

Application of Machine Learning to Beam Diagnostics

optics, diagnostics, controls, simulation

169

E. Fol, J.M. Coello de Portugal, R. Tomás
CERN, Meyrin, 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)

WEPC15

Machine Learning Applied to Predict Transverse Oscillation at SSRF

diagnostics, SRF, injection, storage-ring

512

B. Gao, J. Chen, Y.B. Leng, Y.M. Zhou
SINAP, Shanghai, People’s Republic of China

A fast beam size diagnostic system has been developed at SSRF (Shanghai Synchrotron Radiation Facility) storage ring for turn-by-turn and bunch-by-bunch beam transverse oscillation study. This system is based on visible synchrotron radiation direct imaging system. Currently, this system already has good experimental results. However, this system still has some limitations, the resolution is subject to the point spread function and the speed of online data processing is limited by the complex algorithm. We present a technique that applied machine learning tools to predict transverse oscillation.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IBIC2018-WEPC15

About •

paper received ※ 05 September 2018 paper accepted ※ 13 September 2018 issue date ※ 29 January 2019

Export •

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