U-Net-based surrogate modeling for attosecond X-ray free-electron lasers

TL;DR

This paper introduces a U-Net-based neural network surrogate model that accurately predicts the longitudinal phase space of electron bunches in attosecond X-ray free-electron lasers, enabling real-time diagnostics and control.

Contribution

The work presents a novel U-Net neural network model that predicts 2D longitudinal phase space distributions from accelerator parameters, improving real-time control capabilities.

Findings

Model shows excellent agreement with simulation data.

Potential to enable real-time tuning of attosecond pulse generation.

Enhances diagnostics and control in X-ray free-electron lasers.

Abstract

Attosecond X-ray pulse generation in modern X-ray free-electron lasers relies on strongly compressed, precisely tailored electron bunches, making accurate diagnostics and control of the longitudinal phase space (LPS) essential. In the self-chirping scheme, collective effects in the linac generate a strong energy chirp that is converted into high peak current through pre-undulator compression, enabling isolated attosecond pulse generation. Reliable operation of this scheme depends on precise LPS control and fast diagnostics. In this work, we present a U-Net-based neural network surrogate that predicts two-dimensional LPS distributions directly from accelerator settings. The model exhibits excellent agreement with start-to-end simulation results. These results demonstrate the potential of neural network surrogates to facilitate real-time tuning and control in attosecond X-ray pulse generation.