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.
Original language | English |
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Pages (from-to) | 11806-11819 |
Number of pages | 14 |
Journal | Optics Express |
Volume | 31 |
Issue number | 7 |
DOIs | |
Publication status | Published - 2023 Mar 27 |
Subject classification (UKÄ)
- Computational Mathematics
- Atom and Molecular Physics and Optics