Learning Dynamic Aperture from One-turn Maps

Dynamic aperture evaluation relies on long-term tracking, while existing machine-learning surrogates remain difficult to generalize across machines. We demonstrate that coarse-grained dynamic aperture can be learned directly from suitably encoded one-turn maps. By reformulating dynamic-aperture prediction as an image segmentation problem, a deep surrogate model captures the long-term stability topology and transfers to realistic multidimensional Electron-Ion Collider Electron Storage Ring tracking. Failure analysis identifies a challenging resonant regime in which invariant tori are strongly deformed yet remain unbroken. These results establish a proof-of-principle that practical surrogate models can be constructed from one-turn transport information.