Speaker
Description
Modeling of large-scale research facilities is extremely challenging due to complex physical pro-
cesses and engineering problems. We adopt a data-driven approach to model the longitudinal
phase-space diagnostic beamline at the photoinector of the European XFEL with an encoder-decoder
neural network model. We demonstrate that the model trained only with experimental data can make
high-fidelity predictions of megapixel images for the longitudinal phase-space measurement without
any prior knowledge of photoinjectors and electron beams. The prediction significantly outperforms
existing methods. We also show the scalability and interpretability of the model and propose a
pragmatic way to model a facility with various diagnostics and working points. This opens the door
to a new way of accurately modeling a photoinjector using neural networks and experimental data.
