Learning the lantern: neural network applications to broadband photonic lantern modeling Article Swipe
David Sweeney
,
Barnaby Norris
,
Peter Tuthill
,
R. Scalzo
,
Wei Jin
,
Christopher H. Betters
,
Sergio G. Leon-Saval
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1117/1.jatis.7.2.028007
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1117/1.jatis.7.2.028007
Photonic lanterns allow the decomposition of highly multimodal light into a\nsimplified modal basis such as single-moded and/or few-moded. They are\nincreasingly finding uses in astronomy, optics and telecommunications.\nCalculating propagation through a photonic lantern using traditional algorithms\ntakes $\\sim 1$ hour per simulation on a modern CPU. This paper demonstrates\nthat neural networks can bridge the disparate opto-electronic systems, and when\ntrained can achieve a speed-up of over 5 orders of magnitude. We show that this\napproach can be used to model photonic lanterns with manufacturing defects as\nwell as successfully generalising to polychromatic data. We demonstrate two\nuses of these neural network models, propagating seeing through the photonic\nlantern as well as performing global optimisation for purposes such as photonic\nlantern funnels and photonic lantern nullers.\n
Related Topics
Concepts
Lantern
Broadband
Artificial neural network
Photonics
Computer science
Decomposition
Speedup
Modal
Bridge (graph theory)
Backpropagation
Electronic engineering
Optics
Artificial intelligence
Telecommunications
Physics
Engineering
Parallel computing
Materials science
Ecology
Organic chemistry
Chemistry
Biology
Polymer chemistry
Medicine
Ring (chemistry)
Internal medicine
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1117/1.jatis.7.2.028007
- OA Status
- green
- Cited By
- 4
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3174564331
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3174564331Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1117/1.jatis.7.2.028007Digital Object Identifier
- Title
-
Learning the lantern: neural network applications to broadband photonic lantern modelingWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-06-29Full publication date if available
- Authors
-
David Sweeney, Barnaby Norris, Peter Tuthill, R. Scalzo, Wei Jin, Christopher H. Betters, Sergio G. Leon-SavalList of authors in order
- Landing page
-
https://doi.org/10.1117/1.jatis.7.2.028007Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2108.13274Direct OA link when available
- Concepts
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Lantern, Broadband, Artificial neural network, Photonics, Computer science, Decomposition, Speedup, Modal, Bridge (graph theory), Backpropagation, Electronic engineering, Optics, Artificial intelligence, Telecommunications, Physics, Engineering, Parallel computing, Materials science, Ecology, Organic chemistry, Chemistry, Biology, Polymer chemistry, Medicine, Ring (chemistry), Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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50Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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