High-Fidelity Prediction of Megapixel Longitudinal Phase-Space Images of Electron Beams Using Encoder-Decoder Neural Networks

Abstract

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 longitudinal phase-spacediagnostic beamline at the photoinector of the European XFEL with an encoder-decoder neural-network model. 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 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 or electron beams. The prediction significantly outperforms existing methods. We also show the scalability and interpretability of the model 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 facility with various diagnostics and working points. This opens the door to a way of accurately modeling a photoinjector using neural networks and experimental data. The approach can possibly be extended to the whole accelerator and even other types of scientific facility.

Mixed Diagnostics for Longitudinal Properties of Electron Bunches in a Free-Electron Laser

“…The latest work at the European XFEL injector demonstrated that a deep encoder-decoder neural network can achieve extremely high accuracy in predicting megapixel LPS images using up to three RF phases as input. The current profile, energy spectrum and slice energy spread extracted from the predicted LPS image all show very good agreement with the measurement [18]. In addition, an innovative method was demonstrated to efficiently build large models with multiple distinctive work-FIG.…”

“…During the first 400 epochs, both L LP S and L spectrum are the mean squared error (MSE) and w is set to 1. During the second 400 epochs, L LP S is changed to the multiscale structural similarity index measure (SSIM) [37] with hyperparameters defined in [18] and w is set to 100 empirically in order to make the losses of the two decoders at the same order of magnitude at the end of the training. There are two advantages for using different loss functions for the LPS image decoder in different training phases.…”

“…In our implementation, the training time for a single batch reduces by about 30% when the MSE loss is used. Therefore, the multiscale SSIM loss is only used to fine-tune the model in order to learn high-frequency features in the LPS images [18]. Second, it is found that sometimes the model does not learn correctly near a corner or an edge for all the images when only the multiscale SSIM loss is employed, as shown in Fig.…”

“…and produce accurate and fast predictions [12,13]. Using artificial neural networks as a tool for electron bunch longitudinal property prediction has garnered more and more attention in recent years [14][15][16][17][18]. At the LCLS, early work has demonstrated prediction of LPS images and current profiles at the linac exit using two separated multi-layer perceptrons (MLPs) [14] and a single input parameter.…”

Mixed Diagnostics for Longitudinal Properties of Electron Bunches in a Free-Electron Laser

“…In recent years, machine learning has demonstrated to be able to learn relationships inside a complex system and produce accurate and fast predictions [12,13]. Using artificial neural networks as a tool for electron bunch longitudinal property prediction has garnered more and more attention in recent years [14][15][16][17][18]. At the LCLS, early work has demonstrated prediction of LPS images and current profiles at the linac exit using two separated multi-layer perceptrons (MLPs) [14] and a single input parameter.…”