Ultrafast Pulse Retrieval from Partial FROG Traces Using Implicit Diffusion Models

TL;DR

This paper introduces a diffusion-based generative model for ultrafast pulse retrieval from undersampled FROG traces, significantly improving accuracy and stability over existing methods in challenging measurement regimes.

Contribution

The authors develop a novel diffusion framework that effectively reconstructs ultrashort laser pulses from incomplete FROG data, outperforming prior CNN and sequence models.

Findings

Diffusion model achieves higher fidelity in pulse reconstruction.

Outperforms CNN and Seq2Seq baselines in accuracy and stability.

Enables near real-time pulse retrieval from sparse measurements.

Abstract

Ultrashort laser pulses enable attosecond-scale measurements and drive breakthroughs across science and technology, but their routine use hinges on reliable pulse characterization. Frequency-Resolved Optical Gating (FROG) is a leading solution, forming a spectrogram by scanning the delay between two pulse replicas and recording the nonlinear signal spectrum. In online settings, however, dense delay-frequency scans are costly or impractical-especially for long pulses, wavelength regimes with limited spectrometer coverage (e.g., UV), or hardware with coarse resolution, yielding severely undersampled FROG traces. Existing reconstruction methods struggle in this regime-iterative algorithms are computationally heavy, convolutional networks blur fine structure, and sequence models are unstable when inputs are discontinuous or sparse. We present a generative diffusion framework tailored to recover ultrafast pulse intensity and phase from incomplete FROG measurements. Our model infers missing spectro-temporal content with high fidelity, enabling accurate retrieval from aggressively downsampled inputs. On a simulated benchmark of FROG-pulse pairs, the diffusion approach surpasses strong CNN and Seq2Seq baselines in accuracy and stability while remaining efficient enough for near real-time deployment.