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Convolutional CARMEN: Tomographic Reconstruction for Night Observation

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Hybrid Artificial Intelligent Systems (HAIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11734))

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Abstract

To remove the distortion that the atmosphere causes in the observations performed with extremely large telescopes, correction techniques are required. To tackle this problem, adaptive optics systems uses wave front sensors obtain measures of the atmospheric turbulence and hence, estimate a reconstruction of the atmosphere when this calculation is applied in deformable mirrors, which compensates the aberrated wave front. In Multi Object Adaptive Optics (MOAO), several Shack-Hartmann wave front sensors along with reference guide stars are used to characterize the aberration produced by the atmosphere. Typically, this is a two-step process, where a centroiding algorithm is applied to the image provided by the sensor and the centroids from different Shack-Hartmanns wave front sensors are combined by using a Least Squares algorithm or an Artificial Neural Network, such as the Multi-Layer Perceptron. In this article a new solution based on Convolutional Neural Networks is proposed, which allows to integrate both the centroiding and the tomographic reconstruction in the same algorithm, getting a substantial improvement over the traditional Least Squares algorithm and a similar performance than the Multi-Layer Perceptron, but without the need of previously computing the centroiding algorithm.

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Authors and Affiliations

  1. Department of Prospecting and Exploitation of Mines, University of Oviedo, Independencia 13, 33004, Oviedo, Spain

    Francisco García Riesgo, Carmen Peñalver San Cristóbal & Francisco Javier de Cos Juez

  2. Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, Independencia 13, 33004, Oviedo, Spain

    Francisco García Riesgo, Sergio Luis Suárez Gómez, Fernando Sánchez Lasheras, Carlos González Gutiérrez & Francisco Javier de Cos Juez

  3. Department of Mathematics, University of Oviedo, Calvo Sotelo s/n, 33007, Oviedo, Spain

    Sergio Luis Suárez Gómez & Fernando Sánchez Lasheras

  4. Department of Computer Science, University of Oviedo, Campus of Viesques s/n, 33024, Gijón, Spain

    Carlos González Gutiérrez

Authors
  1. Francisco García Riesgo
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  2. Sergio Luis Suárez Gómez
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  3. Fernando Sánchez Lasheras
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  4. Carlos González Gutiérrez
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  5. Carmen Peñalver San Cristóbal
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  6. Francisco Javier de Cos Juez
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Corresponding author

Correspondence to Fernando Sánchez Lasheras .

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Editors and Affiliations

  1. University of León, León, Spain

    Hilde Pérez García

  2. University of León, León, Spain

    Lidia Sánchez González

  3. University of León, León, Spain

    Manuel Castejón Limas

  4. University of A Coruña, Ferrol, Spain

    Héctor Quintián Pardo

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado Rodríguez

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García Riesgo, F., Suárez Gómez, S.L., Sánchez Lasheras, F., González Gutiérrez, C., Peñalver San Cristóbal, C., de Cos Juez, F.J. (2019). Convolutional CARMEN: Tomographic Reconstruction for Night Observation. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_29

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  • DOI: https://doi.org/10.1007/978-3-030-29859-3_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29858-6

  • Online ISBN: 978-3-030-29859-3

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