Regularization of an autoconvolution problem in ultrashort laser pulse characterization

TL;DR

This paper addresses the ill-posed autoconvolution inverse problem in ultrashort laser pulse characterization, proposing a regularization method for stable phase reconstruction from noisy complex data.

Contribution

It introduces a novel iterative regularization approach tailored for complex-valued autoconvolution problems with noisy data, extending classical models in nonlinear optics.

Findings

Autoconvolution is locally ill-posed everywhere.

Numerical experiments demonstrate the impact of noise on phase reconstruction.

The method effectively stabilizes solutions in synthetic and realistic scenarios.

Abstract

An ill-posed inverse problem of autoconvolution type is investigated. This inverse problem occurs in nonlinear optics in the context of ultrashort laser pulse characterization. The novelty of the mathematical model consists in a physically required extension of the deautoconvolution problem beyond the classical case usually discussed in literature: (i) For measurements of ultrashort laser pulses with the self-diffraction SPIDER method, a stable approximate solution of an autocovolution equation with a complex-valued kernel function is needed. (ii) The considered scenario requires complex functions both, in the solution and the rhs of the integral equation. Since, however, noisy data are available not only for amplitude and phase functions of the rhs, but also for the amplitude of the solution, the stable approximate reconstruction of the associated smooth phase function represents the main goal of the paper. An iterative regularization approach is described that is specifically adapted to the physical situation in pulse characterization, using a non-standard stopping rule for the iteration process of computing regularized solutions. Our approach is illustrated by several case studies for synthetic noisy data and physically realistic complex-valued kernel functions. Based on an example with focus on amplitude perturbations, we show that the autoconvolution equation is locally ill-posed everywhere. To date, the analytical treatment of the impact of noisy data on phase perturbations remains an open question. However, we show its influence with the help of numerical experiments. Moreover, we formulate assertions on the non-uniqueness of the complex-valued autoconvolution problem, at least for the simplified case of a constant kernel. The presented results and figures associated with case studies illustrate the ill-posedness phenomena also for the case of non-trivial complex kernel functions.