Deep learning-based classification of high intensity light patterns in photorefractive crystals
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AuthorsIvanović, Marija D.
Article (Published version)
© 2020 IOP Publishing Ltd.
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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. © 2020 IOP Publishing Ltd.
Keywords:extreme events / convolution neural network / speckling / caustic-like events
Source:Journal of Optics, 2020, 22, 3, 035504-
- Photonics of micro and nano structured materials (RS-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) 
- Capturing and quantitative analysis of multi-scale multi-channel diagnostic data. (EU-691051)