Neural-network-powered pulse reconstruction from one-dimensional interferometric correlation traces
Kolesnichenko, Pavel V. LU
and Zigmantas, Donatas
LU
(2023)
In Optics Express
31(7).
p.11806-11819
- 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,... (More)
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.
(Less)
- author
- Kolesnichenko, Pavel V.
LU
and Zigmantas, Donatas
LU
- organization
- publishing date
- 2023-03-27
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Optics Express
- volume
- 31
- issue
- 7
- pages
- 14 pages
- publisher
- Optical Society of America
- external identifiers
-
- pmid:37155808
- scopus:85153489071
- ISSN
- 1094-4087
- DOI
- 10.1364/OE.479638
- language
- English
- LU publication?
- yes
- id
- 2ec80e6d-23d6-4cbb-bb94-7642f10029f0
- date added to LUP
- 2023-09-22 10:33:09
- date last changed
- 2025-10-19 04:56:02
@article{2ec80e6d-23d6-4cbb-bb94-7642f10029f0,
abstract = {{<p>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.</p>}},
author = {{Kolesnichenko, Pavel V. and Zigmantas, Donatas}},
issn = {{1094-4087}},
language = {{eng}},
month = {{03}},
number = {{7}},
pages = {{11806--11819}},
publisher = {{Optical Society of America}},
series = {{Optics Express}},
title = {{Neural-network-powered pulse reconstruction from one-dimensional interferometric correlation traces}},
url = {{http://dx.doi.org/10.1364/OE.479638}},
doi = {{10.1364/OE.479638}},
volume = {{31}},
year = {{2023}},
}