Learning a Latent Pulse Shape Interface for Photoinjector Laser Systems

TL;DR

This paper introduces a Wasserstein Autoencoder-based framework that learns a continuous, interpretable latent space for laser pulse shaping in photoinjectors, reducing simulation costs and aiding beam optimization.

Contribution

The authors develop a generative model that captures the pulse shape space, enabling smooth interpolation and generalization to real data, improving efficiency in laser pulse design.

Findings

Latent space is continuous and interpretable.

Model accurately reconstructs real experimental pulses.

Enables smooth transitions between pulse types.

Abstract

Controlling the longitudinal laser pulse shape in photoinjectors of Free-Electron Lasers is a powerful lever for optimizing electron beam quality, but systematic exploration of the vast design space is limited by the cost of brute-force pulse propagation simulations. We present a generative modeling framework based on Wasserstein Autoencoders to learn a differentiable latent interface between pulse shaping and downstream beam dynamics. Our empirical findings show that the learned latent space is continuous and interpretable while maintaining high-fidelity reconstructions. Pulse families such as higher-order Gaussians trace coherent trajectories, while standardizing the temporal pulse lengths shows a latent organization correlated with pulse energy. Analysis via principal components and Gaussian Mixture Models reveals a well behaved latent geometry, enabling smooth transitions between distinct pulse types via linear interpolation. The model generalizes from simulated data to real experimental pulse measurements, accurately reconstructing pulses and embedding them consistently into the learned manifold. Overall, the approach reduces reliance on expensive pulse-propagation simulations and facilitates downstream beam dynamics simulation and analysis.