TUP101

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URL	https://doi.org/10.18429/JACoW-NAPAC2025-TUP101
Title	Towards differentiable beam dynamics modeling in BLAST/ImpactX
Authors	A. Huebl, C. Mitchell, R. Lehe, G. Charleux, A. Myers, W. Zhang, J. Qiang, J. Vay Lawrence Berkeley National Laboratory J. Kaiser, C. Hespe Deutsches Elektronen-Synchrotron DESY J. Gonzalez-Aguilera University of Chicago C. Xu Argonne National Laboratory A. Santamaria Garcia University of Liverpool R. Roussel, A. Edelen SLAC National Accelerator Laboratory W. Moses University of Illinois Urbana-Champaign
Abstract	Differentiable simulations are in demand in accelerator physics, demonstrating order-of-magnitude improvements for complex tasks such as many-parameter optimization for accelerator working points and reconstruction of hard-to-measure quantities. At its core, a differentiable simulation does not only solve a forward problem, but additionally provides gradients of output parameters (e.g. beam parameters) with respect to input parameters (e.g. beamline or source parameters). How to effectively program large dynamic simulations differentiably is still an open question, but there is general consensus that a “single-source” approach aided by automatic differentiation (AD) is desirable. Addressing this, there are a) emerging domain-specific languages in machine learning that are intrinsically differentiable, and b) highly-performing & scalable, general-purpose languages like ISO C++ of existing codes. The challenge of approach a) is syntax specialization, which can limit ease of implementation & performance for physics algorithms, while b) requires additional work for AD. Performance is important for modeling high-order beam dynamics and collective effects in accelerators. We compare the fast, modern codes ImpactX (C++/Python) and Cheetah (PyTorch) using traditional, gradient-free modeling. We then show progress in introducing single-source differentiability in ImpactX using modern compiler techniques, producing performant executables for gradient-based and gradient-free modeling.
Paper	download: TUP101.pdf
Cite	BibTeX, LaTeX, Text/Word, RIS, EndNote
Conference	North American Particle Accelerator Conference (NAPAC2025)
Series
Location	Sacramento, CA, USA
Date	10-15 Aug 2025
Publisher	JACoW Publishing, Geneva, Switzerland
Editorial Board	Tor O. Raubenheimer (SLAC), Roark A. Marsh (LLNL), Eric Prebys (UC Davis), Ling Wang (FRIB), Petr Anisimov (LANL), Kip Bishofberger (LANL), Gustavo Bruno (ANL), Zhichu Chen (SARI), Jan Chrin (PSI), Kelly Jaje (ANL), Jaeyu Lee (POSTECH), Magdalena Montes-Loera (SLAC), Mary Saethre (PHENOTYPE), Tasha Summers (SLAC), Kent Wootton (ANL)
Online ISBN	978-3-95450-261-5
Online ISSN	2673-7000
Received	08 August 2025
Revised	14 August 2025
Accepted	14 August 2025
Issued	28 August 2025
DOI	10.18429/JACoW-NAPAC2025-TUP101
Pages	614-617
Copyright	Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI.