Surrogate modeling of the CLIC final-focus system using artificial neural networks

Artificial neural networks can be used for creating surrogate models that replace computationally expensive simulations. In this paper, a model was created subset of the Compact Linear Collider (CLIC) final-focus system. By training on simulation data, we maps sextupole offsets to luminosity and beam sizes, thus replacing intensive tracking beam-beam This then optimizing parameters random walk procedure alignment.