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Phase identification despite amplitude variation in a coherent beam combination using deep learning

Phase identification despite amplitude variation in a coherent beam combination using deep learning
Phase identification despite amplitude variation in a coherent beam combination using deep learning
Coherent beam combination offers the potential for surpassing the power limit of a single fibre laser, as well as achieving agile far-field beam-shaping. However, the spatial beam profile of the combined beam is significantly dependent on the phase of each fibre. Recent results have shown that deep learning can be used to extract phase information from a far-field intensity profile, hence unlocking the potential for real-time control. However, the far-field intensity profile is also dependent on the amplitude of each fibre, and therefore phase identification may also need to occur whilst the fibre amplitudes are not equal. Here, it is shown that a neural network trained to identify phase when all fibres have equal amplitudes can also identify phase values when the amplitudes are not equal, without requiring additional training data.
2770-0208
902 - 916
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Zervas, Michael N.
1840a474-dd50-4a55-ab74-6f086aa3f701
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Zervas, Michael N.
1840a474-dd50-4a55-ab74-6f086aa3f701

Grant-Jacob, James, Mills, Benjamin and Zervas, Michael N. (2023) Phase identification despite amplitude variation in a coherent beam combination using deep learning. Optics Continuum, 2 (4), 902 - 916. (doi:10.1364/OPTCON.485728).

Record type: Article

Abstract

Coherent beam combination offers the potential for surpassing the power limit of a single fibre laser, as well as achieving agile far-field beam-shaping. However, the spatial beam profile of the combined beam is significantly dependent on the phase of each fibre. Recent results have shown that deep learning can be used to extract phase information from a far-field intensity profile, hence unlocking the potential for real-time control. However, the far-field intensity profile is also dependent on the amplitude of each fibre, and therefore phase identification may also need to occur whilst the fibre amplitudes are not equal. Here, it is shown that a neural network trained to identify phase when all fibres have equal amplitudes can also identify phase values when the amplitudes are not equal, without requiring additional training data.

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More information

Accepted/In Press date: 21 March 2023
e-pub ahead of print date: 30 March 2023
Published date: 15 April 2023
Additional Information: Funding Information: Engineering and Physical Sciences Research Council (EP/P027644/1, EP/T026197/1, EP/W028786/1). Publisher Copyright: © 2023 OSA - The Optical Society. All rights reserved.

Identifiers

Local EPrints ID: 476508
URI: http://eprints.soton.ac.uk/id/eprint/476508
ISSN: 2770-0208
PURE UUID: 4aad3c4b-d06e-4b0a-97d4-fde3b89b69f3
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012
ORCID for Michael N. Zervas: ORCID iD orcid.org/0000-0002-0651-4059

Catalogue record

Date deposited: 04 May 2023 17:07
Last modified: 17 Mar 2024 03:22

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