Stabilisation of transverse mode purity in a radially polarised Ho:YAG laser using machine learning
Stabilisation of transverse mode purity in a radially polarised Ho:YAG laser using machine learning
Radially polarised solid-state lasers offer attractive improvements in materials processing applications, but selection and stabilisation of the appropriate radially polarised mode is much more challenging than for the fundamental mode. Here, we demonstrate automated stabilisation of a radially polarised Ho:YAG laser by utilising laser mode analysis computed from a convolutional neural network. The neural network predicts the transverse modal content from single plane intensity images with high accuracy on timescales of a few milliseconds, permitting real-time self-adjustment of the laser cavity. Radially polarised emission has been maintained across a 30 W range of pump power, with the stabilisation of other arbitrary laser modes using the same neural network also demonstrated.
Jefferson-Brain, Thomas
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Barber, Matthew, James
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Coupe, Azaria
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Clarkson, W.A.
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Shardlow, Peter
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28 May 2022
Jefferson-Brain, Thomas
8bce2a02-37a4-4277-a8cb-0c40bde57837
Barber, Matthew, James
5682d70c-71a4-4875-a714-55704b8ac20c
Coupe, Azaria
a94ae3f1-b6ad-4f69-8765-335aedb780e9
Clarkson, W.A.
3b060f63-a303-4fa5-ad50-95f166df1ba2
Shardlow, Peter
9ca17301-8ae7-4307-8bb9-371df461520c
Jefferson-Brain, Thomas, Barber, Matthew, James, Coupe, Azaria, Clarkson, W.A. and Shardlow, Peter
(2022)
Stabilisation of transverse mode purity in a radially polarised Ho:YAG laser using machine learning.
Applied Physics B, 128 (110), [110].
(doi:10.1007/s00340-022-07816-9).
Abstract
Radially polarised solid-state lasers offer attractive improvements in materials processing applications, but selection and stabilisation of the appropriate radially polarised mode is much more challenging than for the fundamental mode. Here, we demonstrate automated stabilisation of a radially polarised Ho:YAG laser by utilising laser mode analysis computed from a convolutional neural network. The neural network predicts the transverse modal content from single plane intensity images with high accuracy on timescales of a few milliseconds, permitting real-time self-adjustment of the laser cavity. Radially polarised emission has been maintained across a 30 W range of pump power, with the stabilisation of other arbitrary laser modes using the same neural network also demonstrated.
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Stabilisation_Accepted_Manuscript
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Jefferson-Brain2022_Article_StabilisationOfTransverseModeP
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Accepted/In Press date: 6 April 2022
Published date: 28 May 2022
Additional Information:
Funding Information:
T. L. Jefferson-Brain acknowledges financial support from EPSRC (1921150). M. J. Barber acknowledges financial support from EPSRC (2115206) and Leonardo UK.
Publisher Copyright:
© 2022, The Author(s).
Identifiers
Local EPrints ID: 457870
URI: http://eprints.soton.ac.uk/id/eprint/457870
ISSN: 0946-2171
PURE UUID: 7b308079-b9df-4fab-9214-2628c4172217
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Date deposited: 21 Jun 2022 18:04
Last modified: 06 Jun 2024 01:50
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Contributors
Author:
Thomas Jefferson-Brain
Author:
Matthew, James Barber
Author:
Azaria Coupe
Author:
W.A. Clarkson
Author:
Peter Shardlow
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