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OPAL a Versatile Tool for Charged Particle Accelerator Simulations

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arxiv 1905.06654 v1 pith:7DOSKT4O submitted 2019-05-16 physics.acc-ph

classification physics.acc-ph
keywords opalbeammodelsparallelacceleratoracceleratorsbeencharge
verification ladder T0 review T1 audit T2 compute T3 formal

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Many sophisticated computer models have been developed to understand the behaviour of particle accelerators. Even these complex models often do not describe the measured data. Interactions of the beam with external fields, other particles in the same beam and the beam walls all present modelling challenges. These can be challenging to model correctly even with modern supercomputers. This paper describes OPAL (Object Oriented Parallel Accelerator Library), a parallel open source tool for charged-particle optics in linear accelerators and rings, including 3D space charge. OPAL is built from the ground up as a parallel application exemplifying the fact that high performance computing is the third leg of science, complementing theory and experiment. Using the MAD language with extensions, OPAL can run on a laptop as well as on the largest high performance computing systems. The OPAL framework makes it easy to add new features in the form of new C++ classes, enabling the modelling of many physics processes and field types. OPAL comes in two flavours: OPAL-cycl: tracks particles with 3D space charge including neighbouring turns in cyclotrons and FFAs with time as the independent variable. OPAL-t: models beam lines, linacs, rf-photo injectors and complete XFELs excluding the undulator. The code is managed through the git distributed version control system. A suite of unit tests have been developed for various parts of OPAL, validating each part of the code independently. System tests validate the overall integration of different elements.

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Cited by 2 Pith papers

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  1. Chaos indicators for non-linear dynamics in circular particle accelerators

    physics.acc-ph 2025-04 conditional novelty 6.0 of 10

    FLIWB and REM chaos indicators applied to realistic HL-LHC lattices can highlight chaotic regions and give a power-law link between Lyapunov time and stability time, supporting cheaper dynamic aperture estimates.

  2. Hierarchical Bayesian Calibration with Bayesian Committee Machine

    stat.CO 2026-08 reject novelty 5.0 of 10

    A scalable hierarchical Bayesian calibration framework using the Bayesian Committee Machine achieves large runtime speedups and is demonstrated on benchmark and accelerator simulation data.

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