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Title: Efficient six-dimensional phase space reconstructions from experimental measurements using generative machine learning

Journal Article · · Physical Review Accelerators and Beams
ORCiD logo [1]; ORCiD logo [2];  [3]; ORCiD logo [3]; ORCiD logo [3]; ORCiD logo [3]; ORCiD logo [2]; ORCiD logo [1]
  1. SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
  2. Univ. of Chicago, IL (United States)
  3. Argonne National Laboratory (ANL), Argonne, IL (United States)
+ Show Author Affiliations

Next-generation accelerator concepts, which hinge on the precise shaping of beam distributions, demand equally precise diagnostic methods capable of reconstructing beam distributions within six-dimensional position-momentum spaces. However, the characterization of intricate features within six-dimensional beam distributions using current diagnostic techniques necessitates a substantial number of measurements, using many hours of valuable beam time. Novel phase space reconstruction techniques are needed to reduce the number of measurements required to reconstruct detailed, high-dimensional beam features in order to resolve complex beam phenomena and as a feedback in precision beam shaping applications. In this study, we present a novel approach to reconstructing detailed six-dimensional phase space distributions from experimental measurements using generative machine learning and differentiable beam dynamics simulations. We demonstrate that this approach can be used to resolve six-dimensional phase space distributions from scratch, using basic beam manipulations and as few as 20 two-dimensional measurements of the beam profile. We also demonstrate an application of the reconstruction method in an experimental setting at the Argonne Wakefield Accelerator, where it is able to reconstruct the beam distribution and accurately predict previously unseen measurements 75× faster than previous methods.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
Sponsoring Organization:
National Energy Research Scientific Computing Center (NERSC); National Science Foundation (NSF); USDOE; USDOE Office of Science (SC); USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
Grant/Contract Number:
AC02-05CH11231; AC02-06CH11357; AC02-76SF00515
OSTI ID:
2440756
Journal Information:
Physical Review Accelerators and Beams, Journal Name: Physical Review Accelerators and Beams Journal Issue: 9 Vol. 27; ISSN 2469-9888
Publisher:
American Physical Society (APS)Copyright Statement
Country of Publication:
United States
Language:
English

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