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

• Verfasst von: Kolesnichenko, Pavel V. [VerfasserIn]
• Verfasst von: Zigmantas, Donatas [VerfasserIn]
• Titel: Neural-network-powered pulse reconstruction from one-dimensional interferometric correlation traces
• Verf.angabe: Pavel V. Kolesnichenko, Donatas Zigmantas
• E-Jahr: 2023
• Jahr: March 24, 2023
• Umfang: 14 S.
• Fussnoten: Gesehen am 16.05.2023
• Titel Quelle: Enthalten in: Optics express
• Ort Quelle: Washington, DC : Optica, 1997
• Jahr Quelle: 2023
• Band/Heft Quelle: 31(2023), 7 vom: März, Seite 11806-11819
• ISSN Quelle: 1094-4087
• DOI: doi:10.1364/OE.479638
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 184552800X

Abstract

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.