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Demonstration of Machine Learning-Based Model-Independent Stabilization of Source Properties in Synchrotron Light Sources

S. C. Leemann, S. Liu, A. Hexemer, M. A. Marcus, C. N. Melton, H. Nishimura, and C. Sun
Phys. Rev. Lett. 123, 194801 – Published 6 November 2019
Physics logo See Synopsis: Noisy Synchrotron? Machine Learning Has the Answer

Abstract

Synchrotron light sources, arguably among the most powerful tools of modern scientific discovery, are presently undergoing a major transformation to provide orders of magnitude higher brightness and transverse coherence enabling the most demanding experiments. In these experiments, overall source stability will soon be limited by achievable levels of electron beam size stability, presently on the order of several microns, which is still 1–2 orders of magnitude larger than already demonstrated stability of source position and current. Until now source size stabilization has been achieved through corrections based on a combination of static predetermined physics models and lengthy calibration measurements, periodically repeated to counteract drift in the accelerator and instrumentation. We now demonstrate for the first time how the application of machine learning allows for a physics- and model-independent stabilization of source size relying only on previously existing instrumentation. Such feed-forward correction based on a neural network that can be continuously online retrained achieves source size stability as low as 0.2μm (0.4%) rms, which results in overall source stability approaching the subpercent noise floor of the most sensitive experiments.

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  • Received 16 May 2019
  • Revised 23 August 2019

DOI:https://doi.org/10.1103/PhysRevLett.123.194801

© 2019 American Physical Society

Physics Subject Headings (PhySH)

Accelerators & Beams

Synopsis

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Noisy Synchrotron? Machine Learning Has the Answer

Published 6 November 2019

Machine-learning algorithms could allow researchers to substantially reduce unwanted fluctuations in the widths of the electron beams produced at synchrotrons.

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Authors & Affiliations

S. C. Leemann*

  • Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA

S. Liu

  • Department of Chemistry, University of California, Berkeley, California 94720, USA

A. Hexemer, M. A. Marcus, C. N. Melton, H. Nishimura, and C. Sun

  • Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA

  • *SCLeemann@lbl.gov
  • liushuai10000@berkeley.edu

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Vol. 123, Iss. 19 — 8 November 2019

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