# Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows

## TL;DR

This paper introduces a normalising flows-based deep learning surrogate model to accurately represent electron bunch distributions in a Free Electron Laser beamline, bridging the gap between simulations and experimental data.

## Contribution

The work presents a novel normalising flows approach for conditional phase-space modeling of electron clouds in FELs, enhancing understanding and analysis capabilities.

## Key findings

- The surrogate model effectively captures electron distribution dynamics.
- The approach reveals potential benefits and limitations for beamline analysis.
- It improves the interpretability of experimental and simulated data.

## Abstract

Understanding and control of Laser-driven Free Electron Lasers remain to be difficult problems that require highly intensive experimental and theoretical research. The gap between simulated and experimentally collected data might complicate studies and interpretation of obtained results. In this work we developed a deep learning based surrogate that could help to fill in this gap. We introduce a surrogate model based on normalising flows for conditional phase-space representation of electron clouds in a FEL beamline. Achieved results let us discuss further benefits and limitations in exploitability of the models to gain deeper understanding of fundamental processes within a beamline.