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URLhttps://doi.org/10.18429/JACoW-IPAC2026-THP5302
TitleDifferentiable, CPU-/GPU-Parallelized, Symplectic Spin-Orbit tracking in SciBmad
Authors
  • J. Devlin, D. Sagan
    Cornell University (CLASSE)
  • G. Hoffstaetter
    Cornell University (CLASSE), Brookhaven National Laboratory
  • M. Signorelli
    Brookhaven National Laboratory
AbstractSciBmad is a new software library for accelerator simulation and design. Three key pillars which drove the development of SciBmad’s tracking code are full differentiability (forwards-, backwards-, and Taylor-mode), high performance, and strict symplecticity. Differentiable integrators enable the trivial extraction of lattice functions and their higher-order generalizations, facilitate the use of machine learning tools, and make optimizations more efficient. The integrators have also been heavily optimized for performance, with SIMD parallelization on the CPU and GPU parallelization for various hardwares. Finally, SciBmad uses Yoshida’s symplectic schemes to integrate the fully nonlinear Hamiltonian for magnetic multipoles, RF cavities, and combinations thereof, including implicit symplectic integration where required. All elements allow for spin tracking, which is guaranteed to be orthogonal due to the generality of Yoshida’s scheme. Additionally, synchrotron radiation can be turned on in all elements. In this case, the use of a symplectic scheme guarantees that all damping results from the synchrotron radiation and not from numerical errors due to non-symplectic integration.
Paperdownload: THP5302.pdf
CiteBibTeX, LaTeX, Text/Word, RIS, EndNote
Conference17th International Particle Accelerator Conference
Series
LocationDeauville, France
Date17-22 May 2026
PublisherJACoW Publishing, Geneva, Switzerland
Editorial BoardEditorial Board
Online ISBN978-3-95450-252-3
Online ISSN2673-5350
Received13 May 2026
Revised20 May 2026
Accepted20 May 2026
Issued20 July 2026
DOI10.18429/JACoW-IPAC2026-THP5302
Pages4699-4702