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DOI:10.18429/JACoW-ICAP2015-TUDBC3
Title Maximum Entropy Tomography Reconstruction
Authors
  • S. Zheng
    CIAE, Beijing, People's Republic of China
  • T. Planche, Y.-N. Rao
    TRIUMF, Canada's National Laboratory for Particle and Nuclear Physics, Vancouver, Canada
Abstract The goal of tomography is to reconstruct a higher dimensional distribution from a series of projections measured in a lower dimensional subspace. In the absence of large number of projections, the maximum entropy algorithm can reconstruct a distribution that maximizes the entropy and simultaneously reproduce all the measured projections exactly. The MENT algorithm has been applied to the reconstruction of the transverse and longitudinal phase space distributions at particle accelerators. Only one-dimensinonal intensity profiles of different beam transfer matrices have to be measured. The article mainly completed the code for the tomographic reconstruction of the longitudinal phase space where non-linear transformations have to be taken into account, and finally introduced the test result of the real data taken from accelerator in triumf for transverse tomography.
Footnotes & References G. Minerbo, MENT: A Maximum Entropy Algorithm for Reconstructing a Source from Projection Data*
J.J. Scheins, Tomographic Reconstruction of Transverse and Longitudinal Phase Space Distributions*
Paper download TUDBC3.PDF [1.169 MB / 3 pages]
Conference ICAP2015, Shanghai, China
Series International Computational Accelerator Physics Conference (12th)
Proceedings Link to full ICAP2015 Proccedings
Session Beam-physics, Magnet Design and Magnetic Measurements
Date 13-Oct-15   15:45–17:15
Main Classification G-1 Beam-physics, Magnet design and Magnetic Measurements
Keywords TRIUMF, factory, beam-transport
Publisher JACoW, Geneva, Switzerland
Editors Kwok C. Ko (SLAC, Menlo Park, CA, USA); Volker R.W. Schaa (GSI, Darmstadt, Germany); Chuanxiang Tang (Tsinghua University, Beijing, China); Heping Yan (SINAP, Shanghai, China)
ISBN 978-3-95450-136-6
Published February 2016
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