Neural-network-powered pulse reconstruction from one-dimensional interferometric cross-correlation traces
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arXiv
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| Format: | Preprint |
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2021
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| _version_ | 1866917576246820864 |
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| author | Kolesnichenko, Pavel V. Zigmantas, Donatas |
| author_facet | Kolesnichenko, Pavel V. Zigmantas, Donatas |
| contents | Any ultrafast optical spectroscopy experiment is usually accompanied by the necessary routine of ultrashort-pulse characterisation. The majority of pulse characterisation 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 problem's over-determined nature. In contrast, the one-dimensional pulse-retrieval problem 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 characterisation using interferometric cross-correlation time traces (determined by the pulses with partial spectral overlap). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2111_01014 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Neural-network-powered pulse reconstruction from one-dimensional interferometric cross-correlation traces Kolesnichenko, Pavel V. Zigmantas, Donatas Optics Data Analysis, Statistics and Probability Instrumentation and Detectors Any ultrafast optical spectroscopy experiment is usually accompanied by the necessary routine of ultrashort-pulse characterisation. The majority of pulse characterisation 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 problem's over-determined nature. In contrast, the one-dimensional pulse-retrieval problem 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 characterisation using interferometric cross-correlation time traces (determined by the pulses with partial spectral overlap). |
| title | Neural-network-powered pulse reconstruction from one-dimensional interferometric cross-correlation traces |
| topic | Optics Data Analysis, Statistics and Probability Instrumentation and Detectors |
| url | https://arxiv.org/abs/2111.01014 |