Deep Learning-Based Autoencoder for Data-Driven Modeling of an RF Photoinjector

Modeling of large-scale research facilities is extremely challenging due to complex physical processes and engineering problems. Here, we adopt a data-driven approach to model the photoinector of European XFEL with a deep learning-based autoencoder. A deep convolutional neural network (decoder) is used to build images measured on the screen from a small feature map generated by another neural network (encoder). We demonstrate that the autoencoder trained only with experimental data can make high-fidelity predictions of megapixel images for the longitudinal phase-space measurement. The prediction significantly outperforms existing methods. We also show the scalability and explicability of the autoencoder by sharing the same decoder with more than one encoder used for different setups of the photoinjector, and propose a pragmatic way to model a photoinjector with various diagnostics and working points. This opens the door to a new way of accurately modeling a photoinjector using neural networks. The approach can possibly be extended to the whole accelerator and even other types of scientific facilities.