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Alignment of higher-order mode solid-state laser systems with machine learning diagnostic assistance

Alignment of higher-order mode solid-state laser systems with machine learning diagnostic assistance
Alignment of higher-order mode solid-state laser systems with machine learning diagnostic assistance
A machine learning algorithm based on a constitutional neural network was trained to be able to predict transverse modal composition in real-time. It can analyse a 128x128 pixel greyscale intensity image in under 3 ms. This provides a fast metric for transverse modal composition - a metric that was lacking for modal content when compared to other characteristics of lasers.
machine learning, lasers, mode, transverse mode, alignment, neural network
Jefferson-Brain, Thomas, Lewis
8bce2a02-37a4-4277-a8cb-0c40bde57837
Coupe, Azaria, Deborah
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Burns, Mark
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Clarkson, William
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Shardlow, Peter
9ca17301-8ae7-4307-8bb9-371df461520c
Jefferson-Brain, Thomas, Lewis
8bce2a02-37a4-4277-a8cb-0c40bde57837
Coupe, Azaria, Deborah
a94ae3f1-b6ad-4f69-8765-335aedb780e9
Burns, Mark
7f7ca346-f31a-46cf-a848-acb4bcae18b9
Clarkson, William
3b060f63-a303-4fa5-ad50-95f166df1ba2
Shardlow, Peter
9ca17301-8ae7-4307-8bb9-371df461520c

Jefferson-Brain, Thomas, Lewis, Coupe, Azaria, Deborah, Burns, Mark, Clarkson, William and Shardlow, Peter (2019) Alignment of higher-order mode solid-state laser systems with machine learning diagnostic assistance. CLEO/Europe-EQEC 2019, Munich, Germany. 23 - 27 Jun 2019.

Record type: Conference or Workshop Item (Poster)

Abstract

A machine learning algorithm based on a constitutional neural network was trained to be able to predict transverse modal composition in real-time. It can analyse a 128x128 pixel greyscale intensity image in under 3 ms. This provides a fast metric for transverse modal composition - a metric that was lacking for modal content when compared to other characteristics of lasers.

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Published date: 24 June 2019
Venue - Dates: CLEO/Europe-EQEC 2019, Munich, Germany, 2019-06-23 - 2019-06-27
Keywords: machine learning, lasers, mode, transverse mode, alignment, neural network

Identifiers

Local EPrints ID: 433478
URI: https://eprints.soton.ac.uk/id/eprint/433478
PURE UUID: a468d051-5540-464e-817d-aeea209c25c4
ORCID for Thomas, Lewis Jefferson-Brain: ORCID iD orcid.org/0000-0002-8838-5640
ORCID for Mark Burns: ORCID iD orcid.org/0000-0003-2039-6025
ORCID for Peter Shardlow: ORCID iD orcid.org/0000-0003-0459-0581

Catalogue record

Date deposited: 23 Aug 2019 16:30
Last modified: 24 Aug 2019 00:31

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