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

Summary, in English

Any ultrafast optical spectroscopy experiment is usually accompanied by the necessary routine of ultrashort-pulse characterization. The majority of pulse characterization 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 the problem’s over-determined nature. In contrast, the one-dimensional pulse-retrieval problem, unless constraints are added, 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 characterization using interferometric correlation time traces determined by the pulses with partial spectral overlap.

• Author: Pavel V. Kolesnichenko; Donatas Zigmantas
• Department/s: Chemical Physics; NanoLund: Centre for Nanoscience; LTH Profile Area: Photon Science and Technology; LTH Profile Area: Nanoscience and Semiconductor Technology
• Publishing year: 2023-03-27
• Language: English
• Pages: 11806-11819
• Publication/Series: Optics Express
• Volume: 31
• Issue: 7
• Links: http://dx.doi.org/10.1364/OE.479638
• Document type: Journal article
• Publisher: Optical Society of America
• Topic: Computational Mathematics; Atom and Molecular Physics and Optics
• Status: Published
• ISBN/ISSN/Other: ISSN: 1094-4087