Neural-network-powered pulse reconstruction from one-dimensional interferometric correlation traces
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 measureme...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
March 24, 2023
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| In: |
Optics express
Year: 2023, Volume: 31, Issue: 7, Pages: 11806-11819 |
| ISSN: | 1094-4087 |
| DOI: | 10.1364/OE.479638 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1364/OE.479638 Verlag, lizenzpflichtig, Volltext: https://opg.optica.org/oe/abstract.cfm?uri=oe-31-7-11806 
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| Author Notes: | Pavel V. Kolesnichenko, Donatas Zigmantas |
| Summary: | 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. |
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| Item Description: | Gesehen am 16.05.2023 |
| Physical Description: | Online Resource |
| ISSN: | 1094-4087 |
| DOI: | 10.1364/OE.479638 |