• Open Access

Transverse phase space tomography in an accelerator test facility using image compression and machine learning

A. Wolski, M. A. Johnson, M. King, B. L. Militsyn, and P. H. Williams
Phys. Rev. Accel. Beams 25, 122803 – Published 15 December 2022

Abstract

We describe a novel technique, based on image compression and machine learning, for transverse phase space tomography in two degrees of freedom in an accelerator beamline. The technique has been used in the CLARA accelerator test facility at Daresbury Laboratory: results from the machine learning method are compared with those from a conventional tomography algorithm (algebraic reconstruction) and applied to the same data. The use of machine learning allows reconstruction of the 4D phase space distribution of the beam to be carried out much more rapidly than using conventional tomography algorithms and also enables the use of image compression to reduce significantly the size of the data sets involved in the analysis. Results from the machine learning technique are at least as good as those from the algebraic reconstruction tomography in characterizing the beam behavior, in terms of the variation of the beam size in response to variation of the quadrupole strengths.

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  • Received 5 September 2022
  • Accepted 14 November 2022

DOI:https://doi.org/10.1103/PhysRevAccelBeams.25.122803

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

Accelerators & Beams

Authors & Affiliations

A. Wolski*

  • University of Liverpool, Liverpool, United Kingdom, and the Cockcroft Institute, Daresbury, United Kingdom

M. A. Johnson, M. King, B. L. Militsyn, and P. H. Williams

  • STFC/ASTeC, Daresbury Laboratory, Daresbury, United Kingdom

  • *a.wolski@liverpool.ac.uk

Article Text

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Issue

Vol. 25, Iss. 12 — December 2022

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