IPAC2019 - List of Authors (Xu, X.Y.)

Paper	Title	Page
WEPGW049	Deep Learning Applied for Multi-Slit Imaging Based Beam Size Monitor	2587
	B. Gao, Y.B. Leng SSRF, Shanghai, People’s Republic of China X.Y. Xu SINAP, Shanghai, People’s Republic of China
	In order to satisfy the requirement of high speed measurement and improve the accuracy of BSM (beam size monitor), multi-slit imaging based BSM has been proposed by SSRF at 2017. However, it is very difficult to deconvolve the image and figure out the beam size, which requires dedicated algorithms to solve this issue. Deep learning is one of the most popular algorithms, which can learn to mimic any distribution of data. In the region of Beam instrumentation, they can be taught to deal with many difficult problem. In this paper, multi-layer neural network is used to process the images from the multi-slit imaging system. Training processes, struct of the neural networks and the result of the experiments will be presented.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-WEPGW049
About •	paper received ※ 15 May 2019 paper accepted ※ 21 May 2019 issue date ※ 21 June 2019
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WEPGW064	Machine Learning Application in Bunch Longitudinal Phase Measurement	2625
	X.Y. Xu, Y.M. Zhou SINAP, Shanghai, People’s Republic of China Y.B. Leng SSRF, Shanghai, People’s Republic of China
	High resolution bunch-by-bunch longitudinal phase measurement has been realized at Shanghai Synchrotron Radiation Facility (SSRF). In order to fully exploit the potency of the bunch phase monitor, the transient state during injection is being further studied. A longitudinal phase fitting method was used to study the synchrotron damping oscillation in injection events, where we can get the energy offsets between the injector and the storage ring, refilled bunch arrived time and the synchrotron damping time. However, manual multi-parameter fitting of nonlinear functions is awfully complex and slow. Machine learning algorithms, such as gradient descent and artificial neural network (ANN) is more suitable to do this fitting. Through these methods, we can quickly obtain more accurate fitting parameters and further realize online measurement of the refilled charge arrived time, energy offsets between the injector and storage ring, and the synchrotron damping time.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-WEPGW064
About •	paper received ※ 13 May 2019 paper accepted ※ 20 May 2019 issue date ※ 21 June 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Deep Learning Applied for Multi-Slit Imaging Based Beam Size Monitor

2587

B. Gao, Y.B. Leng
SSRF, Shanghai, People’s Republic of China
X.Y. Xu
SINAP, Shanghai, People’s Republic of China

In order to satisfy the requirement of high speed measurement and improve the accuracy of BSM (beam size monitor), multi-slit imaging based BSM has been proposed by SSRF at 2017. However, it is very difficult to deconvolve the image and figure out the beam size, which requires dedicated algorithms to solve this issue. Deep learning is one of the most popular algorithms, which can learn to mimic any distribution of data. In the region of Beam instrumentation, they can be taught to deal with many difficult problem. In this paper, multi-layer neural network is used to process the images from the multi-slit imaging system. Training processes, struct of the neural networks and the result of the experiments will be presented.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-WEPGW049

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)

WEPGW064

Machine Learning Application in Bunch Longitudinal Phase Measurement

2625

X.Y. Xu, Y.M. Zhou
SINAP, Shanghai, People’s Republic of China
Y.B. Leng
SSRF, Shanghai, People’s Republic of China

High resolution bunch-by-bunch longitudinal phase measurement has been realized at Shanghai Synchrotron Radiation Facility (SSRF). In order to fully exploit the potency of the bunch phase monitor, the transient state during injection is being further studied. A longitudinal phase fitting method was used to study the synchrotron damping oscillation in injection events, where we can get the energy offsets between the injector and the storage ring, refilled bunch arrived time and the synchrotron damping time. However, manual multi-parameter fitting of nonlinear functions is awfully complex and slow. Machine learning algorithms, such as gradient descent and artificial neural network (ANN) is more suitable to do this fitting. Through these methods, we can quickly obtain more accurate fitting parameters and further realize online measurement of the refilled charge arrived time, energy offsets between the injector and storage ring, and the synchrotron damping time.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-WEPGW064

About •

paper received ※ 13 May 2019 paper accepted ※ 20 May 2019 issue date ※ 21 June 2019

Export •

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