IPAC2019 - List of Authors (Jiang, R.)

Paper	Title	Page
WEPGW045	Application of Clustering by Fast Search and Find of Density Peaks to Beam Diagnostics at SSRF	2581
	R. Jiang, Y.B. Leng SSRF, Shanghai, People’s Republic of China
	With the increased technological complexity of accelera-tors, meeting the demand of beam diagnostics and opera-tion need more powerful and faster methods. And detect-ing the accuracy and stability of beam position moni-tors(BPMs) are important for all kinds of measurement systems and feedback systems in particle accelerator field. As an effective tool for data analysis and automa-tion, the machine learning methods had been used in accelerator physics field, recently. Among machine learn-ing methods, the clustering by fast search and find of density peaks as a typical unsupervised learning algo-rithms could be performed directly without training in arbitrary accelerator systems and could discover un-known patterns in the data. This paper used clustering by fast search and find of density peaks to detect faulty beam position monitor or monitoring beam orbit stability by analysis five typical parameters, that is beta oscilla-tion of X and Y direction(BetaX and BetaY), transverse oscillation of X and Y direction(AmpX and AmpY) and energy oscillation(AmpE). The results showed that cluster-ing by fast search and find of density peaks could classi-fy beam data into different clusters on the basis of their similarity. And that, aberrant run data points could be detected by decision graph. Morever, analysis results demonstrate the characteristic parameters AmpE, AmpX and BetaX amplitude have the same effect to distinguish the faulty BPMs and the AmpY and the BetaY amplitude are also.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-WEPGW045
About •	paper received ※ 15 May 2019 paper accepted ※ 21 May 2019 issue date ※ 21 June 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Application of Clustering by Fast Search and Find of Density Peaks to Beam Diagnostics at SSRF

2581

R. Jiang, Y.B. Leng
SSRF, Shanghai, People’s Republic of China

With the increased technological complexity of accelera-tors, meeting the demand of beam diagnostics and opera-tion need more powerful and faster methods. And detect-ing the accuracy and stability of beam position moni-tors(BPMs) are important for all kinds of measurement systems and feedback systems in particle accelerator field. As an effective tool for data analysis and automa-tion, the machine learning methods had been used in accelerator physics field, recently. Among machine learn-ing methods, the clustering by fast search and find of density peaks as a typical unsupervised learning algo-rithms could be performed directly without training in arbitrary accelerator systems and could discover un-known patterns in the data. This paper used clustering by fast search and find of density peaks to detect faulty beam position monitor or monitoring beam orbit stability by analysis five typical parameters, that is beta oscilla-tion of X and Y direction(BetaX and BetaY), transverse oscillation of X and Y direction(AmpX and AmpY) and energy oscillation(AmpE). The results showed that cluster-ing by fast search and find of density peaks could classi-fy beam data into different clusters on the basis of their similarity. And that, aberrant run data points could be detected by decision graph. Morever, analysis results demonstrate the characteristic parameters AmpE, AmpX and BetaX amplitude have the same effect to distinguish the faulty BPMs and the AmpY and the BetaY amplitude are also.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-WEPGW045

About •

paper received ※ 15 May 2019 paper accepted ※ 21 May 2019 issue date ※ 21 June 2019

Export •

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