Deep learning-based classification of high intensity light patterns in photorefractive crystals
Author
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Ivanovic, Marija
Author
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Mancic, Ana
Author
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Hermann Avigliano, Carla
Author
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Hadzievski, Ljupco
Author
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Maluckov, Aleksandra
Admission date
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2020-04-22T22:57:11Z
Available date
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2020-04-22T22:57:11Z
Publication date
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2020
Cita de ítem
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J. Opt. 22 (2020) 035504 (8pp)
es_ES
Identifier
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10.1088/2040-8986/ab70f0
Identifier
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https://repositorio.uchile.cl/handle/2250/174033
Abstract
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In this paper, we establish a new scheme for identification and classification of high intensity events generated by the propagation of light through a photorefractive SBN crystal. Among these events, which are the inevitable consequence of the development of modulation instability, are speckling and soliton-like patterns. The usual classifiers, developed on statistical measures, such as the significant intensity, often provide only a partial characterization of these events. Here, we try to overcome this deficiency by implementing the convolution neural network method to relate experimental data of light intensity distribution and corresponding numerical outputs with different high intensity regimes. The train and test sets are formed of experimentally obtained intensity profiles at the crystal output facet and corresponding numerical profiles. The accuracy of detection of speckles reaches maximum value of 100%, while the accuracy of solitons and caustic detection is above 97%. These performances are promising for the creation of neural network based routines for prediction of extreme events in wave media.
es_ES
Patrocinador
dc.description.sponsorship
Ministry of Education, Science and Technological Development of Serbia: III 45010
Programa ICM Millennium Institute for Research in Optics (MIRO)
U-Inicia VID Universidad de Chile: UI 004/2018
Comision Nacional de Investigacion Cientifica y Technologica (CONICYT PAI Grant): 77180003
European Union (EU): 691051