![[2602.17263] Learning a Latent Pulse Shape Interface for Photoinjector Laser Systems](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.17263] Learning a Latent Pulse Shape Interface for Photoinjector Laser Systems
Summary
This paper presents a generative modeling framework using Wasserstein Autoencoders to optimize laser pulse shaping in photoinjector systems, enhancing electron beam quality while reducing reliance on costly simulations.
Why It Matters
Optimizing laser pulse shapes is crucial for improving the performance of photoinjector laser systems used in Free-Electron Lasers. This research offers a novel approach that could streamline the design process and enhance experimental outcomes, making it significant for both theoretical and practical applications in laser technology.
Key Takeaways
- Introduces a generative modeling framework for laser pulse shaping.
- Utilizes Wasserstein Autoencoders for efficient design space exploration.
- Demonstrates high-fidelity reconstructions and interpretable latent spaces.
- Enables smooth transitions between different pulse types.
- Reduces dependence on expensive pulse propagation simulations.
Computer Science > Machine Learning arXiv:2602.17263 (cs) [Submitted on 19 Feb 2026] Title:Learning a Latent Pulse Shape Interface for Photoinjector Laser Systems Authors:Alexander Klemps, Denis Ilia, Pradeep Kr. Banerjee, Ye Chen, Henrik Tünnermann, Nihat Ay View a PDF of the paper titled Learning a Latent Pulse Shape Interface for Photoinjector Laser Systems, by Alexander Klemps and 5 other authors View PDF 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 t...
