Speaker
Description
Advanced linear and non-linear optics studies require accurate and efficient tools for high-order beam dynamics computations. MAD-NG provides a unique framework combining linear and nonlinear optics modelling, high-order parametric differential map computation through precise automatic differentiation, and Lie-algebraic operations central to nonlinear normal form analysis, all within a unified environment based on the Generalised Truncated Power Series Algebra (GTPSA). These capabilities enable accurate evaluation of optical functions, chromatic effects, and nonlinear Hamiltonian dynamics. MAD-NG embeds LuaJIT, a high-performance scripting engine, offering automated workflows, symbolic dependencies, and deferred evaluations for efficient lattice design and parametric optimisation. It has been successfully used to improve the LHC beam lifetime at injection (2023) and during collisions (2025) by minimisimg resonant driving terms. Applied to major projects such as the LHC, HL-LHC, and FCC-ee, MAD-NG demonstrates reliability, scalability, and accuracy for large-scale optics and sensitivity studies while providing a flexible, reproducible, and high-performance environment for modern accelerator modelling and advanced beam dynamics research.
| In which format do you inted to submit your paper? | LaTeX |
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