Speaker
Description
Designing optics for accelerators is a continuous process requiring the solution of many multi-dimensional optimization problems. Given the multitude of operating configurations that have to be considered, as well as the increasing size and complexity of modern accelerators, runtime becomes a limiting factor — in particular because computing the required derivatives accounts for most of the total runtime.
In this contribution, we compare two approaches for obtaining these derivatives. The first uses Automatic Differentiation with JAX applied to linear optics, which is highly efficient when a linear description is sufficiently accurate. The second relies on truncated power series algebra (TPSA) as implemented in MAD-NG, offering more precise derivatives and therefore allowing to capture higher-order physical effects. We evaluate their respective strengths and limitations and outline the situations in which each method provides the best balance between accuracy and performance.