Deep learning reconstruction of ultrashort pulses from 2D spatial intensity patterns recorded by an all-in-line system in a single-shot

TL;DR

This paper introduces a deep learning-based method for single-shot ultrashort pulse reconstruction using a simple all-in-line optical system, enabling fast, noise-robust, real-time diagnostics of ultrafast laser pulses.

Contribution

The work presents a novel deep learning approach for ultrashort pulse reconstruction from 2D intensity patterns, eliminating slow iterative methods and enabling real-time, noise-robust diagnostics.

Findings

Successful simulation-based recovery of ultrashort pulses.

Demonstrated robustness to measurement noise.

Reduced reconstruction time compared to traditional methods.

Abstract

We propose a simple all-in-line single-shot scheme for diagnostics of ultrashort laser pulses, consisting of a multi-mode fiber, a nonlinear crystal and a CCD camera. The system records a 2D spatial intensity pattern, from which the pulse shape (amplitude and phase) are recovered, through a fast Deep Learning algorithm. We explore this scheme in simulations and demonstrate the recovery of ultrashort pulses, robustness to noise in measurements and to inaccuracies in the parameters of the system components. Our technique mitigates the need for commonly used iterative optimization reconstruction methods, which are usually slow and hampered by the presence of noise. These features make our concept system advantageous for real time probing of ultrafast processes and noisy conditions. Moreover, this work exemplifies that using deep learning we can unlock new types of systems for pulse recovery.