IPAC2020 - List of Authors (Scheinker, A.)

Paper	Title	Page
WEVIR10	Adaptive Feedback Control and Machine Learning for Particle Accelerators	53
	A. Scheinker LANL, Los Alamos, New Mexico, USA
	The precise control of charged particle beams, such as an electron beam’s longitudinal phase space as well as the maximization of the output power of a free electron laser (FEL), or the minimization of beam loss in accelerators, are challenging tasks. For example, even when all FEL parameter set points are held constant both the beam phase space and the output power have high variance because of the uncertainty and time-variation of thousands of coupled parameters and of the electron distribution coming off of the photo cathode. Similarly, all large accelerators face challenges due to time variation, leading to beam losses and changing behavior even when all accelerator parameters are held fixed. We present recent efforts towards developing machine learning methods along with automatic, model-independent feedback for automatic tuning of charge particle beams in particle accelerators. We present experimental results from the LANSCE linear accelerator at LANL, the EuXFEL, AWAKE at CERN, FACET-II and the LCLS.

DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2020-WEVIR10
About •	paper received ※ 27 May 2020 paper accepted ※ 12 June 2020 issue date ※ 14 June 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Adaptive Feedback Control and Machine Learning for Particle Accelerators

53

A. Scheinker
LANL, Los Alamos, New Mexico, USA

The precise control of charged particle beams, such as an electron beam’s longitudinal phase space as well as the maximization of the output power of a free electron laser (FEL), or the minimization of beam loss in accelerators, are challenging tasks. For example, even when all FEL parameter set points are held constant both the beam phase space and the output power have high variance because of the uncertainty and time-variation of thousands of coupled parameters and of the electron distribution coming off of the photo cathode. Similarly, all large accelerators face challenges due to time variation, leading to beam losses and changing behavior even when all accelerator parameters are held fixed. We present recent efforts towards developing machine learning methods along with automatic, model-independent feedback for automatic tuning of charge particle beams in particle accelerators. We present experimental results from the LANSCE linear accelerator at LANL, the EuXFEL, AWAKE at CERN, FACET-II and the LCLS.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2020-WEVIR10

About •

paper received ※ 27 May 2020 paper accepted ※ 12 June 2020 issue date ※ 14 June 2020

Export •

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