IPAC2022 - List of Authors (Zhang, S.)

Paper	Title	Page
MOPOPT063	Reconstruction of Beam Parameters from Betatron Radiation Using Maximum Likelihood Estimation and Machine Learning	407
	S. Zhang, G. Andonian, C.E. Hansel, P. Manwani, B. Naranjo, M.H. Oruganti, J.B. Rosenzweig, M. Yadav UCLA, Los Angeles, California, USA Ö. Apsimon, C.P. Welsch, M. Yadav The University of Liverpool, Liverpool, United Kingdom
	Funding: US Department of Energy, Division of High Energy Physics, Contract No. DE-SC0009914 STFC Liver-pool Centre for Doctoral Training on Data Intensive Science, grant agreement ST/P006752/1 Betatron radiation that arises during plasma wakefield acceleration can be measured by a UCLA-built Compton spectrometer, which records the energy and angular position of incoming photons. Because information about the properties of the beam is encoded in the betatron radiation, measurements of the radiation can be used to reconstruct beam parameters. One method of extracting information about beam parameters from measurements of radiation is maximum likelihood estimation (MLE), a statistical technique which is used to determine unknown parameters from a distribution of observed data. In addition, machine learning methods, which are increasingly being implemented for different fields of beam diagnostics, can also be applied. We assess the ability of both MLE and other machine learning methods to accurately extract beam parameters from measurements.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOPT063
About •	Received ※ 08 June 2022 — Revised ※ 15 June 2022 — Accepted ※ 24 June 2022 — Issue date ※ 26 June 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Reconstruction of Beam Parameters from Betatron Radiation Using Maximum Likelihood Estimation and Machine Learning

407

S. Zhang, G. Andonian, C.E. Hansel, P. Manwani, B. Naranjo, M.H. Oruganti, J.B. Rosenzweig, M. Yadav
UCLA, Los Angeles, California, USA
Ö. Apsimon, C.P. Welsch, M. Yadav
The University of Liverpool, Liverpool, United Kingdom

Funding: US Department of Energy, Division of High Energy Physics, Contract No. DE-SC0009914 STFC Liver-pool Centre for Doctoral Training on Data Intensive Science, grant agreement ST/P006752/1
Betatron radiation that arises during plasma wakefield acceleration can be measured by a UCLA-built Compton spectrometer, which records the energy and angular position of incoming photons. Because information about the properties of the beam is encoded in the betatron radiation, measurements of the radiation can be used to reconstruct beam parameters. One method of extracting information about beam parameters from measurements of radiation is maximum likelihood estimation (MLE), a statistical technique which is used to determine unknown parameters from a distribution of observed data. In addition, machine learning methods, which are increasingly being implemented for different fields of beam diagnostics, can also be applied. We assess the ability of both MLE and other machine learning methods to accurately extract beam parameters from measurements.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOPT063

About •

Received ※ 08 June 2022 — Revised ※ 15 June 2022 — Accepted ※ 24 June 2022 — Issue date ※ 26 June 2022

Cite •

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