Single step phase optimisation for coherent beam combination using deep learning
Single step phase optimisation for coherent beam combination using deep learning
Coherent beam combination of multiple fibres can be used to overcome limitations such as the power handling capability of single fibre configurations. In such a scheme, the focal intensity profile is critically dependent upon the relative phase of each fibre and so precise control over the phase of each fibre channel is essential. Determining the required phase compensations from the focal intensity profile alone (as measured via a camera) is extremely challenging with a large number of fibres as the phase information is obfuscated. Whilst iterative methods exist for phase retrieval, in practice, due to phase noise within a fibre laser amplification system, a single step process with computational time on the scale of milliseconds is needed. Here, we show how a neural network can be used to identify the phases of each fibre from the focal intensity profile, in a single step of ~ 10 ms, for a simulated 3-ring hexagonal close-packed arrangement, containing 19 separate fibres and subsequently how this enables bespoke beam shaping. In addition, we show that deep learning can be used to determine whether a desired intensity profile is physically possible within the simulation. This, coupled with the demonstrated resilience against simulated experimental noise, indicates a strong potential for the application of deep learning for coherent beam combination.
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
Nilsson, Johan
f41d0948-4ca9-4b93-b44d-680ca0bf157b
Zervas, Michael
1840a474-dd50-4a55-ab74-6f086aa3f701
25 March 2022
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
Nilsson, Johan
f41d0948-4ca9-4b93-b44d-680ca0bf157b
Zervas, Michael
1840a474-dd50-4a55-ab74-6f086aa3f701
Mills, Benjamin, Grant-Jacob, James, Praeger, Matthew, Eason, R.W., Nilsson, Johan and Zervas, Michael
(2022)
Single step phase optimisation for coherent beam combination using deep learning.
Scientific Reports, 12 (5188), [5188].
(doi:10.1038/s41598-022-09172-2).
Abstract
Coherent beam combination of multiple fibres can be used to overcome limitations such as the power handling capability of single fibre configurations. In such a scheme, the focal intensity profile is critically dependent upon the relative phase of each fibre and so precise control over the phase of each fibre channel is essential. Determining the required phase compensations from the focal intensity profile alone (as measured via a camera) is extremely challenging with a large number of fibres as the phase information is obfuscated. Whilst iterative methods exist for phase retrieval, in practice, due to phase noise within a fibre laser amplification system, a single step process with computational time on the scale of milliseconds is needed. Here, we show how a neural network can be used to identify the phases of each fibre from the focal intensity profile, in a single step of ~ 10 ms, for a simulated 3-ring hexagonal close-packed arrangement, containing 19 separate fibres and subsequently how this enables bespoke beam shaping. In addition, we show that deep learning can be used to determine whether a desired intensity profile is physically possible within the simulation. This, coupled with the demonstrated resilience against simulated experimental noise, indicates a strong potential for the application of deep learning for coherent beam combination.
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Single Step Phase Optimisation
- Author's Original
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s41598-022-09172-2
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Submitted date: 4 October 2021
Published date: 25 March 2022
Additional Information:
Funding Information:
B.M. was supported by the Engineering and Physical Research Council Early Career Fellowships Scheme (EP/N03368X/1). M.N.Z. was supported by the Royal Academy of Engineering Research Chairs and Senior Research Fellowships Scheme. This work was supported by the Engineering and Physical Research Council under grant numbers EP/T026197/1 and EP/P027644/1. The authors acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work.
Funding Information:
B.M. was supported by the Engineering and Physical Research Council Early Career Fellowships Scheme (EP/N03368X/1). M.N.Z. was supported by the Royal Academy of Engineering Research Chairs and Senior Research Fellowships Scheme. This work was supported by the Engineering and Physical Research Council under grant numbers EP/T026197/1 and EP/P027644/1. The authors acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work.
Publisher Copyright:
© 2022, The Author(s).
Identifiers
Local EPrints ID: 451494
URI: http://eprints.soton.ac.uk/id/eprint/451494
ISSN: 2045-2322
PURE UUID: 2cf8f3bd-5d4b-47a7-bcd8-0cf58f76940d
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Date deposited: 04 Oct 2021 16:30
Last modified: 17 Mar 2024 03:22
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