Neural-network-powered pulse reconstruction from one-dimensional interferometric cross-correlation traces

TL;DR

This paper introduces a neural network approach to accurately reconstruct ultrashort optical pulses from one-dimensional interferometric cross-correlation traces, overcoming traditional ambiguities and limitations of existing methods.

Contribution

The authors develop a deep neural network that reliably solves the constrained one-dimensional pulse-retrieval problem, enabling fast and complete pulse characterization from interferometric data.

Findings

Neural network achieves unambiguous pulse reconstruction.

Method outperforms traditional iterative algorithms.

Enables rapid and reliable ultrashort pulse characterization.

Abstract

Any ultrafast optical spectroscopy experiment is usually accompanied by the necessary routine of ultrashort-pulse characterisation. The majority of pulse characterisation approaches solve either a one-dimensional (e.g. via interferometry) or a two-dimensional (e.g. via frequency-resolved measurements) problem. Solution of the two-dimensional pulse-retrieval problem is generally more consistent due to problem's over-determined nature. In contrast, the one-dimensional pulse-retrieval problem is impossible to solve unambiguously as ultimately imposed by the fundamental theorem of algebra. In cases where additional constraints are involved, the one-dimensional problem may be possible to solve, however, existing iterative algorithms lack generality, and often stagnate for complicated pulse shapes. Here we use a deep neural network to unambiguously solve a constrained one-dimensional pulse-retrieval problem and show the potential of fast, reliable, and complete pulse characterisation using interferometric cross-correlation time traces (determined by the pulses with partial spectral overlap).